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600 | 600 | ['Lili Mou', 'Rui Yan', 'Ge Li', 'Lu Zhang', 'Zhi Jin'] | 1512.06612v2 | Recent language models, especially those based on recurrent neural networks
(RNNs), make it possible to generate natural language from a learned
probability. Language generation has wide applications including machine
translation, summarization, question answering, conversation systems, etc.
Existing methods typically learn a joint probability of words conditioned on
additional information, which is (either statically or dynamically) fed to
RNN's hidden layer. In many applications, we are likely to impose hard
constraints on the generated texts, i.e., a particular word must appear in the
sentence. Unfortunately, existing approaches could not solve this problem. In
this paper, we propose a novel backward and forward language model. Provided a
specific word, we use RNNs to generate previous words and future words, either
simultaneously or asynchronously, resulting in two model variants. In this way,
the given word could appear at any position in the sentence. Experimental
results show that the generated texts are comparable to sequential LMs in
quality. | Backward and Forward Language Modeling for Constrained Sentence
Generation | 2,015 | http://arxiv.org/pdf/1512.06612v2 | Title Backward Forward Language Modeling Constrained Sentence Generation Summary Recent language model especially based recurrent neural network RNNs make possible generate natural language learned probability Language generation wide application including machine translation summarization question answering conversation system etc Existing method typically learn joint probability word conditioned additional information either statically dynamically fed RNNs hidden layer many application likely impose hard constraint generated text ie particular word must appear sentence Unfortunately existing approach could solve problem paper propose novel backward forward language model Provided specific word use RNNs generate previous word future word either simultaneously asynchronously resulting two model variant way given word could appear position sentence Experimental result show generated text comparable sequential LMs quality Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0713164359331131, 0.04077165201306343, -0.00351351173594594, 0.009156038984656334, -0.050216205418109894, -0.03484518453478813, -0.01834772154688835, 0.015102071687579155, -0.011920073069632053, -0.04726139456033707, 0.04541974887251854, -0.043521977961063385, 0.005748002789914608, 0.04906434938311577, 0.02093552052974701, -0.0617520697414875, 0.012091116048395634, -0.0003824518935289234, 0.006529804784804583, 0.018153807148337364, 0.03634461760520935, 0.023164376616477966, 0.006394251249730587, 0.022860191762447357, -0.011217191815376282, 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601 | 601 | ['Kyuyeon Hwang', 'Minjae Lee', 'Wonyong Sung'] | 1512.08903v1 | In this paper, we propose a context-aware keyword spotting model employing a
character-level recurrent neural network (RNN) for spoken term detection in
continuous speech. The RNN is end-to-end trained with connectionist temporal
classification (CTC) to generate the probabilities of character and
word-boundary labels. There is no need for the phonetic transcription, senone
modeling, or system dictionary in training and testing. Also, keywords can
easily be added and modified by editing the text based keyword list without
retraining the RNN. Moreover, the unidirectional RNN processes an infinitely
long input audio streams without pre-segmentation and keywords are detected
with low-latency before the utterance is finished. Experimental results show
that the proposed keyword spotter significantly outperforms the deep neural
network (DNN) and hidden Markov model (HMM) based keyword-filler model even
with less computations. | Online Keyword Spotting with a Character-Level Recurrent Neural Network | 2,015 | http://arxiv.org/pdf/1512.08903v1 | Title Online Keyword Spotting CharacterLevel Recurrent Neural Network Summary paper propose contextaware keyword spotting model employing characterlevel recurrent neural network RNN spoken term detection continuous speech RNN endtoend trained connectionist temporal classification CTC generate probability character wordboundary label need phonetic transcription senone modeling system dictionary training testing Also keywords easily added modified editing text based keyword list without retraining RNN Moreover unidirectional RNN process infinitely long input audio stream without presegmentation keywords detected lowlatency utterance finished Experimental result show proposed keyword spotter significantly outperforms deep neural network DNN hidden Markov model HMM based keywordfiller model even le computation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.006053483579307795, 0.017226427793502808, 0.04451555758714676, 0.04900750890374184, 0.017928095534443855, -0.02497158572077751, -0.008989205583930016, 0.008729066699743271, -0.047539252787828445, -0.03288761526346207, -0.03752556070685387, -0.04293350502848625, 0.030366219580173492, 0.09057103842496872, 0.0018292726017534733, -0.026361951604485512, 0.02118067629635334, -0.020096585154533386, 0.021795613691210747, 0.011767691001296043, 0.04983307793736458, -0.000725301681086421, -0.07543598115444183, 0.05388583987951279, -0.024119606241583824, 0.0026588672772049904, 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602 | 602 | ['Zhenhao Ge', 'Yufang Sun'] | 1602.07393v1 | Authorship attribution refers to the task of automatically determining the
author based on a given sample of text. It is a problem with a long history and
has a wide range of application. Building author profiles using language models
is one of the most successful methods to automate this task. New language
modeling methods based on neural networks alleviate the curse of dimensionality
and usually outperform conventional N-gram methods. However, there have not
been much research applying them to authorship attribution. In this paper, we
present a novel setup of a Neural Network Language Model (NNLM) and apply it to
a database of text samples from different authors. We investigate how the NNLM
performs on a task with moderate author set size and relatively limited
training and test data, and how the topics of the text samples affect the
accuracy. NNLM achieves nearly 2.5% reduction in perplexity, a measurement of
fitness of a trained language model to the test data. Given 5 random test
sentences, it also increases the author classification accuracy by 3.43% on
average, compared with the N-gram methods using SRILM tools. An open source
implementation of our methodology is freely available at
https://github.com/zge/authorship-attribution/. | Domain Specific Author Attribution Based on Feedforward Neural Network
Language Models | 2,016 | http://arxiv.org/pdf/1602.07393v1 | Title Domain Specific Author Attribution Based Feedforward Neural Network Language Models Summary Authorship attribution refers task automatically determining author based given sample text problem long history wide range application Building author profile using language model one successful method automate task New language modeling method based neural network alleviate curse dimensionality usually outperform conventional Ngram method However much research applying authorship attribution paper present novel setup Neural Network Language Model NNLM apply database text sample different author investigate NNLM performs task moderate author set size relatively limited training test data topic text sample affect accuracy NNLM achieves nearly 25 reduction perplexity measurement fitness trained language model test data Given 5 random test sentence also increase author classification accuracy 343 average compared Ngram method using SRILM tool open source implementation methodology freely available httpsgithubcomzgeauthorshipattribution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.049107592552900314, 0.05301670730113983, -0.03609152138233185, 0.024805482476949692, -0.05954847112298012, -0.019497159868478775, 0.10058499872684479, -0.013518590480089188, 0.01585269533097744, -0.027639785781502724, 0.015150275081396103, -0.0017323875799775124, 0.024015109986066818, 0.03368305042386055, 0.025010712444782257, 0.005749876145273447, -0.01660652458667755, 0.03377573564648628, 0.056472618132829666, 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603 | 603 | ['Liang Lu', 'Lingpeng Kong', 'Chris Dyer', 'Noah A. Smith', 'Steve Renals'] | 1603.00223v2 | We study the segmental recurrent neural network for end-to-end acoustic
modelling. This model connects the segmental conditional random field (CRF)
with a recurrent neural network (RNN) used for feature extraction. Compared to
most previous CRF-based acoustic models, it does not rely on an external system
to provide features or segmentation boundaries. Instead, this model
marginalises out all the possible segmentations, and features are extracted
from the RNN trained together with the segmental CRF. In essence, this model is
self-contained and can be trained end-to-end. In this paper, we discuss
practical training and decoding issues as well as the method to speed up the
training in the context of speech recognition. We performed experiments on the
TIMIT dataset. We achieved 17.3 phone error rate (PER) from the first-pass
decoding --- the best reported result using CRFs, despite the fact that we only
used a zeroth-order CRF and without using any language model. | Segmental Recurrent Neural Networks for End-to-end Speech Recognition | 2,016 | http://arxiv.org/pdf/1603.00223v2 | Title Segmental Recurrent Neural Networks Endtoend Speech Recognition Summary study segmental recurrent neural network endtoend acoustic modelling model connects segmental conditional random field CRF recurrent neural network RNN used feature extraction Compared previous CRFbased acoustic model rely external system provide feature segmentation boundary Instead model marginalises possible segmentation feature extracted RNN trained together segmental CRF essence model selfcontained trained endtoend paper discus practical training decoding issue well method speed training context speech recognition performed experiment TIMIT dataset achieved 173 phone error rate PER firstpass decoding best reported result using CRFs despite fact used zerothorder CRF without using language model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01909462735056877, 0.029673578217625618, 0.037180881947278976, 0.03395266830921173, 0.002734440378844738, -0.03491126373410225, -0.0002440836833557114, 0.007297059986740351, -0.08068285882472992, -0.017752185463905334, -0.02081473171710968, -0.013872994109988213, 0.03581831231713295, 0.017155731096863747, -0.023370908573269844, -0.08939522504806519, -0.005255244672298431, -0.004004042595624924, 0.018761832267045975, 0.007463439833372831, 0.03923661261796951, 0.01829269900918007, -0.030413055792450905, 0.03692750260233879, -0.02414429932832718, 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604 | 604 | ['Lili Mou', 'Zhao Meng', 'Rui Yan', 'Ge Li', 'Yan Xu', 'Lu Zhang', 'Zhi Jin'] | 1603.06111v2 | Transfer learning is aimed to make use of valuable knowledge in a source
domain to help model performance in a target domain. It is particularly
important to neural networks, which are very likely to be overfitting. In some
fields like image processing, many studies have shown the effectiveness of
neural network-based transfer learning. For neural NLP, however, existing
studies have only casually applied transfer learning, and conclusions are
inconsistent. In this paper, we conduct systematic case studies and provide an
illuminating picture on the transferability of neural networks in NLP. | How Transferable are Neural Networks in NLP Applications? | 2,016 | http://arxiv.org/pdf/1603.06111v2 | Title Transferable Neural Networks NLP Applications Summary Transfer learning aimed make use valuable knowledge source domain help model performance target domain particularly important neural network likely overfitting field like image processing many study shown effectiveness neural networkbased transfer learning neural NLP however existing study casually applied transfer learning conclusion inconsistent paper conduct systematic case study provide illuminating picture transferability neural network NLP Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0072393398731946945, 0.044481221586465836, -0.004846601746976376, 0.06205492094159126, -0.013320247642695904, -0.012743615545332432, 0.0037230942398309708, 0.002024505054578185, -0.02883164770901203, -0.030240140855312347, -0.04762844368815422, 0.05009543523192406, -0.008013979531824589, 0.06258271634578705, 0.020630905404686928, -0.1019604280591011, -0.0030245205853134394, 0.03410252183675766, -0.011907411739230156, 0.028123825788497925, -0.0026091132313013077, -0.0207354836165905, -0.005240297876298428, 0.03292924910783768, -0.04246203228831291, 0.0010079985950142145, 0.01843104511499405, -0.021580895408988, 0.02538573183119297, -0.0008934197248890996, 0.009376716800034046, 0.011689748615026474, 0.043396737426519394, -0.017433205619454384, 2.484049673512345e-06, 0.008999456651508808, -0.06145665794610977, -0.006164070218801498, 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605 | 605 | ['Wei-Ning Hsu', 'Yu Zhang', 'James Glass'] | 1603.07044v1 | We apply a general recurrent neural network (RNN) encoder framework to
community question answering (cQA) tasks. Our approach does not rely on any
linguistic processing, and can be applied to different languages or domains.
Further improvements are observed when we extend the RNN encoders with a neural
attention mechanism that encourages reasoning over entire sequences. To deal
with practical issues such as data sparsity and imbalanced labels, we apply
various techniques such as transfer learning and multitask learning. Our
experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score
compared to an information retrieval-based approach, and achieve comparable
performance to a strong handcrafted feature-based method. | Recurrent Neural Network Encoder with Attention for Community Question
Answering | 2,016 | http://arxiv.org/pdf/1603.07044v1 | Title Recurrent Neural Network Encoder Attention Community Question Answering Summary apply general recurrent neural network RNN encoder framework community question answering cQA task approach rely linguistic processing applied different language domain improvement observed extend RNN encoders neural attention mechanism encourages reasoning entire sequence deal practical issue data sparsity imbalanced label apply various technique transfer learning multitask learning experiment SemEval2016 cQA task show 10 improvement MAP score compared information retrievalbased approach achieve comparable performance strong handcrafted featurebased method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 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606 | 606 | ['Saurabh Kataria'] | 1603.07646v1 | We consider the problem of learning distributed representations for tags from
their associated content for the task of tag recommendation. Considering
tagging information is usually very sparse, effective learning from content and
tag association is very crucial and challenging task. Recently, various neural
representation learning models such as WSABIE and its variants show promising
performance, mainly due to compact feature representations learned in a
semantic space. However, their capacity is limited by a linear compositional
approach for representing tags as sum of equal parts and hurt their
performance. In this work, we propose a neural feedback relevance model for
learning tag representations with weighted feature representations. Our
experiments on two widely used datasets show significant improvement for
quality of recommendations over various baselines. | Recursive Neural Language Architecture for Tag Prediction | 2,016 | http://arxiv.org/pdf/1603.07646v1 | Title Recursive Neural Language Architecture Tag Prediction Summary consider problem learning distributed representation tag associated content task tag recommendation Considering tagging information usually sparse effective learning content tag association crucial challenging task Recently various neural representation learning model WSABIE variant show promising performance mainly due compact feature representation learned semantic space However capacity limited linear compositional approach representing tag sum equal part hurt performance work propose neural feedback relevance model learning tag representation weighted feature representation experiment two widely used datasets show significant improvement quality recommendation various baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.046709440648555756, -0.005000155884772539, -0.046266429126262665, 0.02858760952949524, 0.001003048848360777, 0.0013018466997891665, -0.013201013207435608, -0.004298039246350527, 0.05208227410912514, -0.023654785007238388, -0.025690404698252678, -0.0348496213555336, 0.024195024743676186, 0.07179399579763412, 0.014678412117064, -0.00562423188239336, 0.028939049690961838, -0.010827668942511082, 0.05229843035340309, -0.003132255980744958, 0.04234648495912552, 0.01432371698319912, 0.0189591646194458, 0.06554217636585236, -0.03476925566792488, -0.014525850303471088, 0.01438379380851984, -0.0340668223798275, 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607 | 607 | ['Rohit Prabhavalkar', 'Ouais Alsharif', 'Antoine Bruguier', 'Ian McGraw'] | 1603.08042v2 | We study the problem of compressing recurrent neural networks (RNNs). In
particular, we focus on the compression of RNN acoustic models, which are
motivated by the goal of building compact and accurate speech recognition
systems which can be run efficiently on mobile devices. In this work, we
present a technique for general recurrent model compression that jointly
compresses both recurrent and non-recurrent inter-layer weight matrices. We
find that the proposed technique allows us to reduce the size of our Long
Short-Term Memory (LSTM) acoustic model to a third of its original size with
negligible loss in accuracy. | On the Compression of Recurrent Neural Networks with an Application to
LVCSR acoustic modeling for Embedded Speech Recognition | 2,016 | http://arxiv.org/pdf/1603.08042v2 | Title Compression Recurrent Neural Networks Application LVCSR acoustic modeling Embedded Speech Recognition Summary study problem compressing recurrent neural network RNNs particular focus compression RNN acoustic model motivated goal building compact accurate speech recognition system run efficiently mobile device work present technique general recurrent model compression jointly compress recurrent nonrecurrent interlayer weight matrix find proposed technique allows u reduce size Long ShortTerm Memory LSTM acoustic model third original size negligible loss accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01828327216207981, -0.004037776961922646, 0.015006584115326405, 0.054580144584178925, -0.012824430130422115, -0.023773901164531708, 0.027628332376480103, 0.022657198831439018, -0.06416162848472595, 0.023214837536215782, -0.03965678811073303, -0.013697794638574123, 0.04727723076939583, -0.015872197225689888, 0.007471926510334015, -0.001838129130192101, 0.005701861809939146, 0.00877582747489214, 0.00800557341426611, 0.008378888480365276, 0.028014590963721275, -0.010744377039372921, 0.011782201007008553, 0.05349387228488922, 0.004868357442319393, -0.0012191010173410177, -0.008090320974588394, 0.020205121487379074, -0.03764826059341431, -0.04638095945119858, -0.003041474847123027, 0.003497287631034851, 0.049422815442085266, 0.03341308608651161, 2.1559117158176377e-06, 0.03493695706129074, 0.021522799506783485, -0.006784701719880104, 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608 | 608 | ['Caglar Gulcehre', 'Sungjin Ahn', 'Ramesh Nallapati', 'Bowen Zhou', 'Yoshua Bengio'] | 1603.08148v3 | The problem of rare and unknown words is an important issue that can
potentially influence the performance of many NLP systems, including both the
traditional count-based and the deep learning models. We propose a novel way to
deal with the rare and unseen words for the neural network models using
attention. Our model uses two softmax layers in order to predict the next word
in conditional language models: one predicts the location of a word in the
source sentence, and the other predicts a word in the shortlist vocabulary. At
each time-step, the decision of which softmax layer to use choose adaptively
made by an MLP which is conditioned on the context.~We motivate our work from a
psychological evidence that humans naturally have a tendency to point towards
objects in the context or the environment when the name of an object is not
known.~We observe improvements on two tasks, neural machine translation on the
Europarl English to French parallel corpora and text summarization on the
Gigaword dataset using our proposed model. | Pointing the Unknown Words | 2,016 | http://arxiv.org/pdf/1603.08148v3 | Title Pointing Unknown Words Summary problem rare unknown word important issue potentially influence performance many NLP system including traditional countbased deep learning model propose novel way deal rare unseen word neural network model using attention model us two softmax layer order predict next word conditional language model one predicts location word source sentence predicts word shortlist vocabulary timestep decision softmax layer use choose adaptively made MLP conditioned contextWe motivate work psychological evidence human naturally tendency point towards object context environment name object knownWe observe improvement two task neural machine translation Europarl English French parallel corpus text summarization Gigaword dataset using proposed model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0698966532945633, 0.012464814819395542, 0.01565462164580822, 0.032125454396009445, 0.0012413514778017998, 0.0022937203757464886, -0.005260268691927195, 0.0024114649277180433, -0.021239766851067543, -0.04228523001074791, 0.010414793156087399, -0.006894045043736696, 0.017795942723751068, 0.015037612058222294, 0.04137621074914932, -0.06338874995708466, 0.01658269762992859, 0.0032354884315282106, 0.0038570764008909464, 0.025359151884913445, 0.023244231939315796, 0.02298750728368759, 0.00364408316090703, 0.036311522126197815, -0.01529789250344038, -0.00606272928416729, -0.010460696183145046, 0.0027063926681876183, 0.022003980353474617, 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609 | 609 | ['Zhenyao Zhu', 'Jesse H. Engel', 'Awni Hannun'] | 1603.09509v2 | Deep learning has dramatically improved the performance of speech recognition
systems through learning hierarchies of features optimized for the task at
hand. However, true end-to-end learning, where features are learned directly
from waveforms, has only recently reached the performance of hand-tailored
representations based on the Fourier transform. In this paper, we detail an
approach to use convolutional filters to push past the inherent tradeoff of
temporal and frequency resolution that exists for spectral representations. At
increased computational cost, we show that increasing temporal resolution via
reduced stride and increasing frequency resolution via additional filters
delivers significant performance improvements. Further, we find more efficient
representations by simultaneously learning at multiple scales, leading to an
overall decrease in word error rate on a difficult internal speech test set by
20.7% relative to networks with the same number of parameters trained on
spectrograms. | Learning Multiscale Features Directly From Waveforms | 2,016 | http://arxiv.org/pdf/1603.09509v2 | Title Learning Multiscale Features Directly Waveforms Summary Deep learning dramatically improved performance speech recognition system learning hierarchy feature optimized task hand However true endtoend learning feature learned directly waveform recently reached performance handtailored representation based Fourier transform paper detail approach use convolutional filter push past inherent tradeoff temporal frequency resolution exists spectral representation increased computational cost show increasing temporal resolution via reduced stride increasing frequency resolution via additional filter delivers significant performance improvement find efficient representation simultaneously learning multiple scale leading overall decrease word error rate difficult internal speech test set 207 relative network number parameter trained spectrogram Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.033016979694366455, 0.059531424194574356, 0.01716112531721592, 0.058574970811605453, 0.017842482775449753, 5.215239434619434e-05, 0.06738042831420898, 0.010663111694157124, -0.023073263466358185, 0.011679023504257202, -0.08431396633386612, -0.05607355386018753, 0.037901535630226135, 0.06979480385780334, 0.009736104868352413, -0.07163964956998825, -0.02279752492904663, -0.039039891213178635, -0.018424030393362045, -0.007215558551251888, 0.019212113693356514, -0.004787637386471033, -0.0387989841401577, 0.025836393237113953, -0.06769516319036484, 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610 | 610 | ['Petr Baudis', 'Silvestr Stanko', 'Jan Sedivy'] | 1605.04655v2 | We consider the problem of Recognizing Textual Entailment within an
Information Retrieval context, where we must simultaneously determine the
relevancy as well as degree of entailment for individual pieces of evidence to
determine a yes/no answer to a binary natural language question.
We compare several variants of neural networks for sentence embeddings in a
setting of decision-making based on evidence of varying relevance. We propose a
basic model to integrate evidence for entailment, show that joint training of
the sentence embeddings to model relevance and entailment is feasible even with
no explicit per-evidence supervision, and show the importance of evaluating
strong baselines. We also demonstrate the benefit of carrying over text
comprehension model trained on an unrelated task for our small datasets.
Our research is motivated primarily by a new open dataset we introduce,
consisting of binary questions and news-based evidence snippets. We also apply
the proposed relevance-entailment model on a similar task of ranking
multiple-choice test answers, evaluating it on a preliminary dataset of school
test questions as well as the standard MCTest dataset, where we improve the
neural model state-of-art. | Joint Learning of Sentence Embeddings for Relevance and Entailment | 2,016 | http://arxiv.org/pdf/1605.04655v2 | Title Joint Learning Sentence Embeddings Relevance Entailment Summary consider problem Recognizing Textual Entailment within Information Retrieval context must simultaneously determine relevancy well degree entailment individual piece evidence determine yesno answer binary natural language question compare several variant neural network sentence embeddings setting decisionmaking based evidence varying relevance propose basic model integrate evidence entailment show joint training sentence embeddings model relevance entailment feasible even explicit perevidence supervision show importance evaluating strong baseline also demonstrate benefit carrying text comprehension model trained unrelated task small datasets research motivated primarily new open dataset introduce consisting binary question newsbased evidence snippet also apply proposed relevanceentailment model similar task ranking multiplechoice test answer evaluating preliminary dataset school test question well standard MCTest dataset improve neural model stateofart Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06912939995527267, 0.0009819197002798319, -0.000473639986012131, 0.045569971203804016, -0.05108325555920601, 0.03289928287267685, -0.02245612069964409, -0.008449180983006954, 0.021231716498732567, -0.041814446449279785, 0.0102603854611516, 0.051032230257987976, -0.028879690915346146, 0.03999879211187363, -0.0005955322994850576, 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611 | 611 | ['Xiaodong Gu', 'Hongyu Zhang', 'Dongmei Zhang', 'Sunghun Kim'] | 1605.08535v3 | Developers often wonder how to implement a certain functionality (e.g., how
to parse XML files) using APIs. Obtaining an API usage sequence based on an
API-related natural language query is very helpful in this regard. Given a
query, existing approaches utilize information retrieval models to search for
matching API sequences. These approaches treat queries and APIs as bag-of-words
(i.e., keyword matching or word-to-word alignment) and lack a deep
understanding of the semantics of the query.
We propose DeepAPI, a deep learning based approach to generate API usage
sequences for a given natural language query. Instead of a bags-of-words
assumption, it learns the sequence of words in a query and the sequence of
associated APIs. DeepAPI adapts a neural language model named RNN
Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length
context vector, and generates an API sequence based on the context vector. We
also augment the RNN Encoder-Decoder by considering the importance of
individual APIs. We empirically evaluate our approach with more than 7 million
annotated code snippets collected from GitHub. The results show that our
approach generates largely accurate API sequences and outperforms the related
approaches. | Deep API Learning | 2,016 | http://arxiv.org/pdf/1605.08535v3 | Title Deep API Learning Summary Developers often wonder implement certain functionality eg parse XML file using APIs Obtaining API usage sequence based APIrelated natural language query helpful regard Given query existing approach utilize information retrieval model search matching API sequence approach treat query APIs bagofwords ie keyword matching wordtoword alignment lack deep understanding semantics query propose DeepAPI deep learning based approach generate API usage sequence given natural language query Instead bagsofwords assumption learns sequence word query sequence associated APIs DeepAPI adapts neural language model named RNN EncoderDecoder encodes word sequence user query fixedlength context vector generates API sequence based context vector also augment RNN EncoderDecoder considering importance individual APIs empirically evaluate approach 7 million annotated code snippet collected GitHub result show approach generates largely accurate API sequence outperforms related approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.022035649046301842, 0.08039530366659164, -0.022920427843928337, 0.05839980021119118, -0.023591330274939537, -0.025041567161679268, -0.018716687336564064, 0.004967098589986563, 0.017906080931425095, -0.03410175442695618, 0.015619850717484951, 0.014092438854277134, 0.019767960533499718, 0.09026051312685013, 0.00809093751013279, -0.029618408530950546, 0.02686324343085289, 0.022160092368721962, 0.028574297204613686, 0.04676632583141327, 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612 | 612 | ['Ozan Caglayan', 'Walid Aransa', 'Yaxing Wang', 'Marc Masana', 'Mercedes García-Martínez', 'Fethi Bougares', 'Loïc Barrault', 'Joost van de Weijer'] | 1605.09186v4 | This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in order to estimate the usefulness of multimodal data for human
machine translation and image description generation. Our systems obtained the
best results for both tasks according to the automatic evaluation metrics BLEU
and METEOR. | Does Multimodality Help Human and Machine for Translation and Image
Captioning? | 2,016 | http://arxiv.org/pdf/1605.09186v4 | Title Multimodality Help Human Machine Translation Image Captioning Summary paper present system developed LIUM CVC WMT16 Multimodal Machine Translation challenge explored various comparative method namely phrasebased system attentional recurrent neural network model trained using monomodal multimodal data also performed human evaluation order estimate usefulness multimodal data human machine translation image description generation system obtained best result task according automatic evaluation metric BLEU METEOR Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed 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613 | 613 | ['Alexis Conneau', 'Holger Schwenk', 'Loïc Barrault', 'Yann Lecun'] | 1606.01781v2 | The dominant approach for many NLP tasks are recurrent neural networks, in
particular LSTMs, and convolutional neural networks. However, these
architectures are rather shallow in comparison to the deep convolutional
networks which have pushed the state-of-the-art in computer vision. We present
a new architecture (VDCNN) for text processing which operates directly at the
character level and uses only small convolutions and pooling operations. We are
able to show that the performance of this model increases with depth: using up
to 29 convolutional layers, we report improvements over the state-of-the-art on
several public text classification tasks. To the best of our knowledge, this is
the first time that very deep convolutional nets have been applied to text
processing. | Very Deep Convolutional Networks for Text Classification | 2,016 | http://arxiv.org/pdf/1606.01781v2 | Title Deep Convolutional Networks Text Classification Summary dominant approach many NLP task recurrent neural network particular LSTMs convolutional neural network However architecture rather shallow comparison deep convolutional network pushed stateoftheart computer vision present new architecture VDCNN text processing operates directly character level us small convolution pooling operation able show performance model increase depth using 29 convolutional layer report improvement stateoftheart several public text classification task best knowledge first time deep convolutional net applied text processing Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.060872286558151245, 0.024899516254663467, 0.0042010704055428505, 0.07925429940223694, -0.017536798492074013, 0.012691543437540531, 0.03025246411561966, 0.02713078074157238, 0.017733626067638397, -0.07143654674291611, -0.014239005744457245, -0.006598727311939001, -0.004835042171180248, 0.044520456343889236, -0.008369509130716324, -0.05190165713429451, 0.01772454008460045, 0.035216204822063446, 0.021472163498401642, 0.03738377243280411, 0.011142811737954617, 0.029498137533664703, 0.03985057771205902, 0.05112925171852112, -0.00846274383366108, -0.011175544932484627, 0.027283934876322746, -0.0026210015639662743, -0.0203599464148283, -0.03990819305181503, 0.04837192967534065, 0.05944285914301872, 0.023488888517022133, 0.06599850207567215, 2.277084831803222e-06, 0.01171105820685625, 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614 | 614 | ['Marco Dinarelli', 'Isabelle Tellier'] | 1606.02555v1 | In this paper we study different types of Recurrent Neural Networks (RNN) for
sequence labeling tasks. We propose two new variants of RNNs integrating
improvements for sequence labeling, and we compare them to the more traditional
Elman and Jordan RNNs. We compare all models, either traditional or new, on
four distinct tasks of sequence labeling: two on Spoken Language Understanding
(ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the
Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are
always more effective than the others. | Improving Recurrent Neural Networks For Sequence Labelling | 2,016 | http://arxiv.org/pdf/1606.02555v1 | Title Improving Recurrent Neural Networks Sequence Labelling Summary paper study different type Recurrent Neural Networks RNN sequence labeling task propose two new variant RNNs integrating improvement sequence labeling compare traditional Elman Jordan RNNs compare model either traditional new four distinct task sequence labeling two Spoken Language Understanding ATIS MEDIA two POS tagging French Treebank FTB Penn Treebank PTB corpus result show new variant RNNs always effective others Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew 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615 | 615 | ['Marek Rei', 'Ronan Cummins'] | 1606.03144v1 | We investigate the task of assessing sentence-level prompt relevance in
learner essays. Various systems using word overlap, neural embeddings and
neural compositional models are evaluated on two datasets of learner writing.
We propose a new method for sentence-level similarity calculation, which learns
to adjust the weights of pre-trained word embeddings for a specific task,
achieving substantially higher accuracy compared to other relevant baselines. | Sentence Similarity Measures for Fine-Grained Estimation of Topical
Relevance in Learner Essays | 2,016 | http://arxiv.org/pdf/1606.03144v1 | Title Sentence Similarity Measures FineGrained Estimation Topical Relevance Learner Essays Summary investigate task assessing sentencelevel prompt relevance learner essay Various system using word overlap neural embeddings neural compositional model evaluated two datasets learner writing propose new method sentencelevel similarity calculation learns adjust weight pretrained word embeddings specific task achieving substantially higher accuracy compared relevant baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype 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616 | 616 | ['Hwaran Lee', 'Geonmin Kim', 'Ho-Gyeong Kim', 'Sang-Hoon Oh', 'Soo-Young Lee'] | 1606.03207v2 | Convolutional neural networks (CNNs) with convolutional and pooling
operations along the frequency axis have been proposed to attain invariance to
frequency shifts of features. However, this is inappropriate with regard to the
fact that acoustic features vary in frequency. In this paper, we contend that
convolution along the time axis is more effective. We also propose the addition
of an intermap pooling (IMP) layer to deep CNNs. In this layer, filters in each
group extract common but spectrally variant features, then the layer pools the
feature maps of each group. As a result, the proposed IMP CNN can achieve
insensitivity to spectral variations characteristic of different speakers and
utterances. The effectiveness of the IMP CNN architecture is demonstrated on
several LVCSR tasks. Even without speaker adaptation techniques, the
architecture achieved a WER of 12.7% on the SWB part of the Hub5'2000
evaluation test set, which is competitive with other state-of-the-art methods. | Deep CNNs along the Time Axis with Intermap Pooling for Robustness to
Spectral Variations | 2,016 | http://arxiv.org/pdf/1606.03207v2 | Title Deep CNNs along Time Axis Intermap Pooling Robustness Spectral Variations Summary Convolutional neural network CNNs convolutional pooling operation along frequency axis proposed attain invariance frequency shift feature However inappropriate regard fact acoustic feature vary frequency paper contend convolution along time axis effective also propose addition intermap pooling IMP layer deep CNNs layer filter group extract common spectrally variant feature layer pool feature map group result proposed IMP CNN achieve insensitivity spectral variation characteristic different speaker utterance effectiveness IMP CNN architecture demonstrated several LVCSR task Even without speaker adaptation technique architecture achieved WER 127 SWB part Hub52000 evaluation test set competitive stateoftheart method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.006704570259898901, -0.0024397079832851887, 0.002484459662809968, 0.047621820122003555, -0.01375389751046896, -0.014747148379683495, 0.04437816143035889, 0.00294478889554739, -0.07536257058382034, 0.023285526782274246, -0.07044918090105057, -0.013373246416449547, 0.03883817791938782, 0.020465411245822906, 0.004897863604128361, -0.03407517075538635, -0.010263723321259022, 0.025215255096554756, 0.010145358741283417, -0.02199825830757618, 0.029807396233081818, -0.036734215915203094, -0.07203567028045654, 0.01403614692389965, -0.023149121552705765, -0.005807599984109402, -0.017666801810264587, -0.010625863447785378, 0.040038395673036575, 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617 | 617 | ['Dimitrios Alikaniotis', 'Helen Yannakoudakis', 'Marek Rei'] | 1606.04289v2 | Automated Text Scoring (ATS) provides a cost-effective and consistent
alternative to human marking. However, in order to achieve good performance,
the predictive features of the system need to be manually engineered by human
experts. We introduce a model that forms word representations by learning the
extent to which specific words contribute to the text's score. Using Long-Short
Term Memory networks to represent the meaning of texts, we demonstrate that a
fully automated framework is able to achieve excellent results over similar
approaches. In an attempt to make our results more interpretable, and inspired
by recent advances in visualizing neural networks, we introduce a novel method
for identifying the regions of the text that the model has found more
discriminative. | Automatic Text Scoring Using Neural Networks | 2,016 | http://arxiv.org/pdf/1606.04289v2 | Title Automatic Text Scoring Using Neural Networks Summary Automated Text Scoring ATS provides costeffective consistent alternative human marking However order achieve good performance predictive feature system need manually engineered human expert introduce model form word representation learning extent specific word contribute text score Using LongShort Term Memory network represent meaning text demonstrate fully automated framework able achieve excellent result similar approach attempt make result interpretable inspired recent advance visualizing neural network introduce novel method identifying region text model found discriminative Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03240957856178284, -0.006253769621253014, -0.019459666684269905, 0.02273603342473507, -0.02264132909476757, -0.011064781807363033, 0.018832484260201454, 0.024143334478139877, 0.0169227197766304, -0.059367869049310684, -0.016813043504953384, 0.010927054099738598, 0.061029329895973206, 0.0573597326874733, 0.007465907372534275, -0.03169374540448189, -0.010010731406509876, 0.02612670138478279, 0.02460837922990322, 0.03335961699485779, 0.03129490092396736, 0.00426724087446928, 0.01212682668119669, 0.029731139540672302, -0.026713350787758827, -0.005650108680129051, 0.022880861535668373, -0.04777051880955696, 0.020106922835111618, -0.029628241434693336, 0.009162906557321548, 0.05440618842840195, -0.028390824794769287, 0.009083354845643044, 2.283526328028529e-06, 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618 | 618 | ['Albert Zeyer', 'Patrick Doetsch', 'Paul Voigtlaender', 'Ralf Schlüter', 'Hermann Ney'] | 1606.06871v2 | We present a comprehensive study of deep bidirectional long short-term memory
(LSTM) recurrent neural network (RNN) based acoustic models for automatic
speech recognition (ASR). We study the effect of size and depth and train
models of up to 8 layers. We investigate the training aspect and study
different variants of optimization methods, batching, truncated
backpropagation, different regularization techniques such as dropout and $L_2$
regularization, and different gradient clipping variants.
The major part of the experimental analysis was performed on the Quaero
corpus. Additional experiments also were performed on the Switchboard corpus.
Our best LSTM model has a relative improvement in word error rate of over 14\%
compared to our best feed-forward neural network (FFNN) baseline on the Quaero
task. On this task, we get our best result with an 8 layer bidirectional LSTM
and we show that a pretraining scheme with layer-wise construction helps for
deep LSTMs.
Finally we compare the training calculation time of many of the presented
experiments in relation with recognition performance.
All the experiments were done with RETURNN, the RWTH extensible training
framework for universal recurrent neural networks in combination with RASR, the
RWTH ASR toolkit. | A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic
Modeling in Speech Recognition | 2,016 | http://arxiv.org/pdf/1606.06871v2 | Title Comprehensive Study Deep Bidirectional LSTM RNNs Acoustic Modeling Speech Recognition Summary present comprehensive study deep bidirectional long shortterm memory LSTM recurrent neural network RNN based acoustic model automatic speech recognition ASR study effect size depth train model 8 layer investigate training aspect study different variant optimization method batching truncated backpropagation different regularization technique dropout L2 regularization different gradient clipping variant major part experimental analysis performed Quaero corpus Additional experiment also performed Switchboard corpus best LSTM model relative improvement word error rate 14 compared best feedforward neural network FFNN baseline Quaero task task get best result 8 layer bidirectional LSTM show pretraining scheme layerwise construction help deep LSTMs Finally compare training calculation time many presented experiment relation recognition performance experiment done RETURNN RWTH extensible training framework universal recurrent neural network combination RASR RWTH ASR toolkit Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.018957188352942467, -0.0008593597449362278, 0.02219080552458763, 0.09828613698482513, -0.019801892340183258, -0.0029993506614118814, 0.02846546471118927, -0.02588002383708954, -0.02469421923160553, -0.025839192792773247, -0.06362873315811157, -0.053315840661525726, 0.0055857026018202305, 0.011284314095973969, 0.002963716397061944, -0.06338094174861908, 0.02271326817572117, 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619 | 619 | ['Yoon Kim', 'Alexander M. Rush'] | 1606.07947v4 | Neural machine translation (NMT) offers a novel alternative formulation of
translation that is potentially simpler than statistical approaches. However to
reach competitive performance, NMT models need to be exceedingly large. In this
paper we consider applying knowledge distillation approaches (Bucila et al.,
2006; Hinton et al., 2015) that have proven successful for reducing the size of
neural models in other domains to the problem of NMT. We demonstrate that
standard knowledge distillation applied to word-level prediction can be
effective for NMT, and also introduce two novel sequence-level versions of
knowledge distillation that further improve performance, and somewhat
surprisingly, seem to eliminate the need for beam search (even when applied on
the original teacher model). Our best student model runs 10 times faster than
its state-of-the-art teacher with little loss in performance. It is also
significantly better than a baseline model trained without knowledge
distillation: by 4.2/1.7 BLEU with greedy decoding/beam search. Applying weight
pruning on top of knowledge distillation results in a student model that has 13
times fewer parameters than the original teacher model, with a decrease of 0.4
BLEU. | Sequence-Level Knowledge Distillation | 2,016 | http://arxiv.org/pdf/1606.07947v4 | Title SequenceLevel Knowledge Distillation Summary Neural machine translation NMT offer novel alternative formulation translation potentially simpler statistical approach However reach competitive performance NMT model need exceedingly large paper consider applying knowledge distillation approach Bucila et al 2006 Hinton et al 2015 proven successful reducing size neural model domain problem NMT demonstrate standard knowledge distillation applied wordlevel prediction effective NMT also introduce two novel sequencelevel version knowledge distillation improve performance somewhat surprisingly seem eliminate need beam search even applied original teacher model best student model run 10 time faster stateoftheart teacher little loss performance also significantly better baseline model trained without knowledge distillation 4217 BLEU greedy decodingbeam search Applying weight pruning top knowledge distillation result student model 13 time fewer parameter original teacher model decrease 04 BLEU Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.042545102536678314, 0.07262073457241058, 0.002678351476788521, 0.030915237963199615, -0.016466815024614334, -0.013895583339035511, 0.00313385808840394, 0.009221713058650494, -0.07044472545385361, -0.038467105478048325, 0.016061589121818542, -0.0013897559838369489, 0.025277916342020035, 0.02924548089504242, 0.024057792499661446, -0.09509803354740143, 0.00010619321255944669, -0.003774982877075672, 0.0033694463782012463, 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620 | 620 | ['Madhusudan Lakshmana', 'Sundararajan Sellamanickam', 'Shirish Shevade', 'Keerthi Selvaraj'] | 1608.00466v2 | The state-of-the-art CNN models give good performance on sentence
classification tasks. The purpose of this work is to empirically study
desirable properties such as semantic coherence, attention mechanism and
reusability of CNNs in these tasks. Semantically coherent kernels are
preferable as they are a lot more interpretable for explaining the decision of
the learned CNN model. We observe that the learned kernels do not have semantic
coherence. Motivated by this observation, we propose to learn kernels with
semantic coherence using clustering scheme combined with Word2Vec
representation and domain knowledge such as SentiWordNet. We suggest a
technique to visualize attention mechanism of CNNs for decision explanation
purpose. Reusable property enables kernels learned on one problem to be used in
another problem. This helps in efficient learning as only a few additional
domain specific filters may have to be learned. We demonstrate the efficacy of
our core ideas of learning semantically coherent kernels and leveraging
reusable kernels for efficient learning on several benchmark datasets.
Experimental results show the usefulness of our approach by achieving
performance close to the state-of-the-art methods but with semantic and
reusable properties. | Learning Semantically Coherent and Reusable Kernels in Convolution
Neural Nets for Sentence Classification | 2,016 | http://arxiv.org/pdf/1608.00466v2 | Title Learning Semantically Coherent Reusable Kernels Convolution Neural Nets Sentence Classification Summary stateoftheart CNN model give good performance sentence classification task purpose work empirically study desirable property semantic coherence attention mechanism reusability CNNs task Semantically coherent kernel preferable lot interpretable explaining decision learned CNN model observe learned kernel semantic coherence Motivated observation propose learn kernel semantic coherence using clustering scheme combined Word2Vec representation domain knowledge SentiWordNet suggest technique visualize attention mechanism CNNs decision explanation purpose Reusable property enables kernel learned one problem used another problem help efficient learning additional domain specific filter may learned demonstrate efficacy core idea learning semantically coherent kernel leveraging reusable kernel efficient learning several benchmark datasets Experimental result show usefulness approach achieving performance close stateoftheart method semantic reusable property Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.028136268258094788, 0.0006274341722019017, 0.009030177257955074, 0.08677586168050766, 9.5728573796805e-05, -0.03109797276556492, -0.007404872216284275, -0.012728176079690456, -0.003021592739969492, -0.06996281445026398, 0.011573974043130875, 0.06878609210252762, -0.01573127880692482, 0.03351372852921486, 0.001849544234573841, -0.0626978874206543, 0.03168894723057747, 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621 | 621 | ['Patrick Doetsch', 'Albert Zeyer', 'Paul Voigtlaender', 'Ilya Kulikov', 'Ralf Schlüter', 'Hermann Ney'] | 1608.00895v2 | In this work we release our extensible and easily configurable neural network
training software. It provides a rich set of functional layers with a
particular focus on efficient training of recurrent neural network topologies
on multiple GPUs. The source of the software package is public and freely
available for academic research purposes and can be used as a framework or as a
standalone tool which supports a flexible configuration. The software allows to
train state-of-the-art deep bidirectional long short-term memory (LSTM) models
on both one dimensional data like speech or two dimensional data like
handwritten text and was used to develop successful submission systems in
several evaluation campaigns. | RETURNN: The RWTH Extensible Training framework for Universal Recurrent
Neural Networks | 2,016 | http://arxiv.org/pdf/1608.00895v2 | Title RETURNN RWTH Extensible Training framework Universal Recurrent Neural Networks Summary work release extensible easily configurable neural network training software provides rich set functional layer particular focus efficient training recurrent neural network topology multiple GPUs source software package public freely available academic research purpose used framework standalone tool support flexible configuration software allows train stateoftheart deep bidirectional long shortterm memory LSTM model one dimensional data like speech two dimensional data like handwritten text used develop successful submission system several evaluation campaign Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie 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622 | 622 | ['Kyuyeon Hwang', 'Wonyong Sung'] | 1609.03777v2 | Recurrent neural network (RNN) based character-level language models (CLMs)
are extremely useful for modeling out-of-vocabulary words by nature. However,
their performance is generally much worse than the word-level language models
(WLMs), since CLMs need to consider longer history of tokens to properly
predict the next one. We address this problem by proposing hierarchical RNN
architectures, which consist of multiple modules with different timescales.
Despite the multi-timescale structures, the input and output layers operate
with the character-level clock, which allows the existing RNN CLM training
approaches to be directly applicable without any modifications. Our CLM models
show better perplexity than Kneser-Ney (KN) 5-gram WLMs on the One Billion Word
Benchmark with only 2% of parameters. Also, we present real-time
character-level end-to-end speech recognition examples on the Wall Street
Journal (WSJ) corpus, where replacing traditional mono-clock RNN CLMs with the
proposed models results in better recognition accuracies even though the number
of parameters are reduced to 30%. | Character-Level Language Modeling with Hierarchical Recurrent Neural
Networks | 2,016 | http://arxiv.org/pdf/1609.03777v2 | Title CharacterLevel Language Modeling Hierarchical Recurrent Neural Networks Summary Recurrent neural network RNN based characterlevel language model CLMs extremely useful modeling outofvocabulary word nature However performance generally much worse wordlevel language model WLMs since CLMs need consider longer history token properly predict next one address problem proposing hierarchical RNN architecture consist multiple module different timescales Despite multitimescale structure input output layer operate characterlevel clock allows existing RNN CLM training approach directly applicable without modification CLM model show better perplexity KneserNey KN 5gram WLMs One Billion Word Benchmark 2 parameter Also present realtime characterlevel endtoend speech recognition example Wall Street Journal WSJ corpus replacing traditional monoclock RNN CLMs proposed model result better recognition accuracy even though number parameter reduced 30 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03231010586023331, 0.066754549741745, 0.01192980632185936, 0.039426058530807495, -0.03793578967452049, 0.006929038558155298, 0.048987630754709244, 0.01880742609500885, -0.0144269410520792, -0.035498570650815964, -0.007920471951365471, -0.05754922702908516, 0.032995760440826416, 0.03209879249334335, 0.004534644540399313, -0.08438754081726074, 0.02680325135588646, -0.03831161931157112, 0.001213796902447939, -0.003520174417644739, -0.004208665806800127, -0.00021489299251697958, -0.0059348419308662415, 0.05079992488026619, 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623 | 623 | ['Zhiyuan Tang', 'Lantian Li', 'Dong Wang'] | 1609.08337v1 | Research on multilingual speech recognition remains attractive yet
challenging. Recent studies focus on learning shared structures under the
multi-task paradigm, in particular a feature sharing structure. This approach
has been found effective to improve performance on each individual language.
However, this approach is only useful when the deployed system supports just
one language. In a true multilingual scenario where multiple languages are
allowed, performance will be significantly reduced due to the competition among
languages in the decoding space. This paper presents a multi-task recurrent
model that involves a multilingual speech recognition (ASR) component and a
language recognition (LR) component, and the ASR component is informed of the
language information by the LR component, leading to a language-aware
recognition. We tested the approach on an English-Chinese bilingual recognition
task. The results show that the proposed multi-task recurrent model can improve
performance of multilingual recognition systems. | Multi-task Recurrent Model for True Multilingual Speech Recognition | 2,016 | http://arxiv.org/pdf/1609.08337v1 | Title Multitask Recurrent Model True Multilingual Speech Recognition Summary Research multilingual speech recognition remains attractive yet challenging Recent study focus learning shared structure multitask paradigm particular feature sharing structure approach found effective improve performance individual language However approach useful deployed system support one language true multilingual scenario multiple language allowed performance significantly reduced due competition among language decoding space paper present multitask recurrent model involves multilingual speech recognition ASR component language recognition LR component ASR component informed language information LR component leading languageaware recognition tested approach EnglishChinese bilingual recognition task result show proposed multitask recurrent model improve performance multilingual recognition system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.011886225081980228, -0.02644840069115162, 0.0017623017774894834, 0.07945913821458817, -0.020982034504413605, 0.03868953511118889, 0.042908940464258194, -0.020795507356524467, 0.008639167994260788, -0.023615295067429543, -0.09359521418809891, -0.06473927199840546, 0.04473632946610451, 0.0031332438811659813, -0.006202876567840576, -0.04437415301799774, 0.011135815642774105, -0.038439564406871796, -0.005476691760122776, -0.014281493611633778, 0.007499278988689184, 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624 | 624 | ['A. Hassan', 'M. R. Amin', 'N. Mohammed', 'A. K. A. Azad'] | 1610.00369v2 | Sentiment Analysis (SA) is an action research area in the digital age. With
rapid and constant growth of online social media sites and services, and the
increasing amount of textual data such as - statuses, comments, reviews etc.
available in them, application of automatic SA is on the rise. However, most of
the research works on SA in natural language processing (NLP) are based on
English language. Despite being the sixth most widely spoken language in the
world, Bangla still does not have a large and standard dataset. Because of
this, recent research works in Bangla have failed to produce results that can
be both comparable to works done by others and reusable as stepping stones for
future researchers to progress in this field. Therefore, we first tried to
provide a textual dataset - that includes not just Bangla, but Romanized Bangla
texts as well, is substantial, post-processed and multiple validated, ready to
be used in SA experiments. We tested this dataset in Deep Recurrent model,
specifically, Long Short Term Memory (LSTM), using two types of loss functions
- binary crossentropy and categorical crossentropy, and also did some
experimental pre-training by using data from one validation to pre-train the
other and vice versa. Lastly, we documented the results along with some
analysis on them, which were promising. | Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep
Recurrent models | 2,016 | http://arxiv.org/pdf/1610.00369v2 | Title Sentiment Analysis Bangla Romanized Bangla Text BRBT using Deep Recurrent model Summary Sentiment Analysis SA action research area digital age rapid constant growth online social medium site service increasing amount textual data status comment review etc available application automatic SA rise However research work SA natural language processing NLP based English language Despite sixth widely spoken language world Bangla still large standard dataset recent research work Bangla failed produce result comparable work done others reusable stepping stone future researcher progress field Therefore first tried provide textual dataset includes Bangla Romanized Bangla text well substantial postprocessed multiple validated ready used SA experiment tested dataset Deep Recurrent model specifically Long Short Term Memory LSTM using two type loss function binary crossentropy categorical crossentropy also experimental pretraining using data one validation pretrain vice versa Lastly documented result along analysis promising Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05192713811993599, 0.027055339887738228, -0.0023078983649611473, 0.05712750926613808, -0.03185954689979553, 0.005102986469864845, -0.018143607303500175, 0.008185272105038166, -0.0019556041806936264, -0.05489837005734444, -0.005273478105664253, -0.059093259274959564, 0.01299042534083128, 0.07535676658153534, -0.02079404518008232, -0.02774219401180744, 0.020425191149115562, 0.04429235681891441, 0.04130254685878754, 0.013419744558632374, 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625 | 625 | ['Marek Rei', 'Gamal K. O. Crichton', 'Sampo Pyysalo'] | 1611.04361v1 | Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is able to dynamically decide how much information to use from a
word- or character-level component. We evaluated different architectures on a
range of sequence labeling datasets, and character-level extensions were found
to improve performance on every benchmark. In addition, the proposed
attention-based architecture delivered the best results even with a smaller
number of trainable parameters. | Attending to Characters in Neural Sequence Labeling Models | 2,016 | http://arxiv.org/pdf/1611.04361v1 | Title Attending Characters Neural Sequence Labeling Models Summary Sequence labeling architecture use word embeddings capturing similarity suffer handling previously unseen rare word investigate characterlevel extension model propose novel architecture combining alternative word representation using attention mechanism model able dynamically decide much information use word characterlevel component evaluated different architecture range sequence labeling datasets characterlevel extension found improve performance every benchmark addition proposed attentionbased architecture delivered best result even smaller number trainable parameter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.061423782259225845, 0.026141155511140823, 0.010138444602489471, 0.027237990871071815, 0.009262291714549065, 0.022990919649600983, 0.0010185353457927704, -0.012745029293000698, 0.026322072371840477, -0.007542262319475412, -0.021471505984663963, -0.05359182134270668, 0.028383905068039894, 0.04412228241562843, 0.0492861308157444, -0.07764638215303421, 0.04427860304713249, 0.011284268461167812, 0.039824314415454865, 0.04457029700279236, 0.03612712025642395, -0.005421402398496866, 0.02461155503988266, 0.03994985297322273, -0.011699538677930832, -0.003978439141064882, 0.00473231915384531, -0.01176653616130352, 0.029012631624937057, -0.06534940004348755, 0.03440696746110916, 0.06460767984390259, 0.039477258920669556, 0.035418424755334854, 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626 | 626 | ['Volkan Cirik', 'Eduard Hovy', 'Louis-Philippe Morency'] | 1611.06204v1 | Curriculum Learning emphasizes the order of training instances in a
computational learning setup. The core hypothesis is that simpler instances
should be learned early as building blocks to learn more complex ones. Despite
its usefulness, it is still unknown how exactly the internal representation of
models are affected by curriculum learning. In this paper, we study the effect
of curriculum learning on Long Short-Term Memory (LSTM) networks, which have
shown strong competency in many Natural Language Processing (NLP) problems. Our
experiments on sentiment analysis task and a synthetic task similar to sequence
prediction tasks in NLP show that curriculum learning has a positive effect on
the LSTM's internal states by biasing the model towards building constructive
representations i.e. the internal representation at the previous timesteps are
used as building blocks for the final prediction. We also find that smaller
models significantly improves when they are trained with curriculum learning.
Lastly, we show that curriculum learning helps more when the amount of training
data is limited. | Visualizing and Understanding Curriculum Learning for Long Short-Term
Memory Networks | 2,016 | http://arxiv.org/pdf/1611.06204v1 | Title Visualizing Understanding Curriculum Learning Long ShortTerm Memory Networks Summary Curriculum Learning emphasizes order training instance computational learning setup core hypothesis simpler instance learned early building block learn complex one Despite usefulness still unknown exactly internal representation model affected curriculum learning paper study effect curriculum learning Long ShortTerm Memory LSTM network shown strong competency many Natural Language Processing NLP problem experiment sentiment analysis task synthetic task similar sequence prediction task NLP show curriculum learning positive effect LSTMs internal state biasing model towards building constructive representation ie internal representation previous timesteps used building block final prediction also find smaller model significantly improves trained curriculum learning Lastly show curriculum learning help amount training data limited Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.003692563623189926, -0.04951534792780876, -0.04816649109125137, 0.01879819668829441, 0.009423155337572098, 0.03182114660739899, 0.018345246091485023, -0.05829724669456482, -0.017054418101906776, -0.014998605474829674, -0.0012486756313592196, 0.014945280738174915, 0.03155054897069931, 0.0015359147218987346, 0.04603245481848717, -0.04778693616390228, 0.03205198422074318, -0.020732667297124863, -0.013541754335165024, 0.03361818194389343, 0.009672281332314014, -0.01847689598798752, -0.028895365074276924, 0.06463489681482315, 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627 | 627 | ['Tom Sercu', 'Vaibhava Goel'] | 1611.09288v2 | In computer vision pixelwise dense prediction is the task of predicting a
label for each pixel in the image. Convolutional neural networks achieve good
performance on this task, while being computationally efficient. In this paper
we carry these ideas over to the problem of assigning a sequence of labels to a
set of speech frames, a task commonly known as framewise classification. We
show that dense prediction view of framewise classification offers several
advantages and insights, including computational efficiency and the ability to
apply batch normalization. When doing dense prediction we pay specific
attention to strided pooling in time and introduce an asymmetric dilated
convolution, called time-dilated convolution, that allows for efficient and
elegant implementation of pooling in time. We show results using time-dilated
convolutions in a very deep VGG-style CNN with batch normalization on the Hub5
Switchboard-2000 benchmark task. With a big n-gram language model, we achieve
7.7% WER which is the best single model single-pass performance reported so
far. | Dense Prediction on Sequences with Time-Dilated Convolutions for Speech
Recognition | 2,016 | http://arxiv.org/pdf/1611.09288v2 | Title Dense Prediction Sequences TimeDilated Convolutions Speech Recognition Summary computer vision pixelwise dense prediction task predicting label pixel image Convolutional neural network achieve good performance task computationally efficient paper carry idea problem assigning sequence label set speech frame task commonly known framewise classification show dense prediction view framewise classification offer several advantage insight including computational efficiency ability apply batch normalization dense prediction pay specific attention strided pooling time introduce asymmetric dilated convolution called timedilated convolution allows efficient elegant implementation pooling time show result using timedilated convolution deep VGGstyle CNN batch normalization Hub5 Switchboard2000 benchmark task big ngram language model achieve 77 WER best single model singlepass performance reported far Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01365703996270895, 0.03957163169980049, 0.006407111417502165, 0.04693054407835007, -0.008529930375516415, -0.023427637293934822, 0.06719914823770523, 0.002023087115958333, -0.053115736693143845, -0.005807191599160433, -0.014297999441623688, -0.03579714149236679, 0.032626617699861526, 0.06886547803878784, 0.017840595915913582, -0.11167633533477783, 0.0028774964157491922, -0.013305512256920338, 0.0166360791772604, 0.005274661350995302, 0.01632332243025303, -0.014410513453185558, -0.02259630337357521, 0.06132602319121361, -0.03220260143280029, 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628 | 628 | ['Kartik Audhkhasi', 'Andrew Rosenberg', 'Abhinav Sethy', 'Bhuvana Ramabhadran', 'Brian Kingsbury'] | 1701.04313v1 | End-to-end (E2E) systems have achieved competitive results compared to
conventional hybrid hidden Markov model (HMM)-deep neural network based
automatic speech recognition (ASR) systems. Such E2E systems are attractive due
to the lack of dependence on alignments between input acoustic and output
grapheme or HMM state sequence during training. This paper explores the design
of an ASR-free end-to-end system for text query-based keyword search (KWS) from
speech trained with minimal supervision. Our E2E KWS system consists of three
sub-systems. The first sub-system is a recurrent neural network (RNN)-based
acoustic auto-encoder trained to reconstruct the audio through a
finite-dimensional representation. The second sub-system is a character-level
RNN language model using embeddings learned from a convolutional neural
network. Since the acoustic and text query embeddings occupy different
representation spaces, they are input to a third feed-forward neural network
that predicts whether the query occurs in the acoustic utterance or not. This
E2E ASR-free KWS system performs respectably despite lacking a conventional ASR
system and trains much faster. | End-to-End ASR-free Keyword Search from Speech | 2,017 | http://arxiv.org/pdf/1701.04313v1 | Title EndtoEnd ASRfree Keyword Search Speech Summary Endtoend E2E system achieved competitive result compared conventional hybrid hidden Markov model HMMdeep neural network based automatic speech recognition ASR system E2E system attractive due lack dependence alignment input acoustic output grapheme HMM state sequence training paper explores design ASRfree endtoend system text querybased keyword search KWS speech trained minimal supervision E2E KWS system consists three subsystem first subsystem recurrent neural network RNNbased acoustic autoencoder trained reconstruct audio finitedimensional representation second subsystem characterlevel RNN language model using embeddings learned convolutional neural network Since acoustic text query embeddings occupy different representation space input third feedforward neural network predicts whether query occurs acoustic utterance E2E ASRfree KWS system performs respectably despite lacking conventional ASR system train much faster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.019321588799357414, 0.02940099500119686, 0.027120331302285194, 0.0550609789788723, -0.006799870170652866, -0.020971085876226425, 0.003670979058369994, 0.0041054971516132355, -0.023339271545410156, -0.05908973887562752, -0.03312704339623451, 0.004711388610303402, -0.012850308790802956, 0.06760329753160477, 0.0077787828631699085, -0.021992921829223633, 0.013645962812006474, -0.02099149115383625, -0.022030867636203766, 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629 | 629 | ['Sam Wiseman', 'Sumit Chopra', "Marc'Aurelio Ranzato", 'Arthur Szlam', 'Ruoyu Sun', 'Soumith Chintala', 'Nicolas Vasilache'] | 1702.04770v1 | While Truncated Back-Propagation through Time (BPTT) is the most popular
approach to training Recurrent Neural Networks (RNNs), it suffers from being
inherently sequential (making parallelization difficult) and from truncating
gradient flow between distant time-steps. We investigate whether Target
Propagation (TPROP) style approaches can address these shortcomings.
Unfortunately, extensive experiments suggest that TPROP generally underperforms
BPTT, and we end with an analysis of this phenomenon, and suggestions for
future work. | Training Language Models Using Target-Propagation | 2,017 | http://arxiv.org/pdf/1702.04770v1 | Title Training Language Models Using TargetPropagation Summary Truncated BackPropagation Time BPTT popular approach training Recurrent Neural Networks RNNs suffers inherently sequential making parallelization difficult truncating gradient flow distant timesteps investigate whether Target Propagation TPROP style approach address shortcoming Unfortunately extensive experiment suggest TPROP generally underperforms BPTT end analysis phenomenon suggestion future work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed 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630 | 630 | ['Sercan O. Arik', 'Mike Chrzanowski', 'Adam Coates', 'Gregory Diamos', 'Andrew Gibiansky', 'Yongguo Kang', 'Xian Li', 'John Miller', 'Andrew Ng', 'Jonathan Raiman', 'Shubho Sengupta', 'Mohammad Shoeybi'] | 1702.07825v2 | We present Deep Voice, a production-quality text-to-speech system constructed
entirely from deep neural networks. Deep Voice lays the groundwork for truly
end-to-end neural speech synthesis. The system comprises five major building
blocks: a segmentation model for locating phoneme boundaries, a
grapheme-to-phoneme conversion model, a phoneme duration prediction model, a
fundamental frequency prediction model, and an audio synthesis model. For the
segmentation model, we propose a novel way of performing phoneme boundary
detection with deep neural networks using connectionist temporal classification
(CTC) loss. For the audio synthesis model, we implement a variant of WaveNet
that requires fewer parameters and trains faster than the original. By using a
neural network for each component, our system is simpler and more flexible than
traditional text-to-speech systems, where each component requires laborious
feature engineering and extensive domain expertise. Finally, we show that
inference with our system can be performed faster than real time and describe
optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x
speedups over existing implementations. | Deep Voice: Real-time Neural Text-to-Speech | 2,017 | http://arxiv.org/pdf/1702.07825v2 | Title Deep Voice Realtime Neural TexttoSpeech Summary present Deep Voice productionquality texttospeech system constructed entirely deep neural network Deep Voice lay groundwork truly endtoend neural speech synthesis system comprises five major building block segmentation model locating phoneme boundary graphemetophoneme conversion model phoneme duration prediction model fundamental frequency prediction model audio synthesis model segmentation model propose novel way performing phoneme boundary detection deep neural network using connectionist temporal classification CTC loss audio synthesis model implement variant WaveNet requires fewer parameter train faster original using neural network component system simpler flexible traditional texttospeech system component requires laborious feature engineering extensive domain expertise Finally show inference system performed faster real time describe optimized WaveNet inference kernel CPU GPU achieve 400x speedup existing implementation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.009917370043694973, 0.047601792961359024, 0.015423452481627464, 0.02271011844277382, 0.007447405252605677, -0.04852304980158806, 0.039579831063747406, 0.010334247723221779, -0.051909007132053375, -0.01405063085258007, -0.04730913043022156, -0.025458768010139465, 0.044778551906347275, 0.0770290419459343, 0.02070579119026661, -0.09342982620000839, 0.00865989364683628, 0.00978696346282959, 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631 | 631 | ['Zichao Yang', 'Zhiting Hu', 'Ruslan Salakhutdinov', 'Taylor Berg-Kirkpatrick'] | 1702.08139v2 | Recent work on generative modeling of text has found that variational
auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM
language models (Bowman et al., 2015). This negative result is so far poorly
understood, but has been attributed to the propensity of LSTM decoders to
ignore conditioning information from the encoder. In this paper, we experiment
with a new type of decoder for VAE: a dilated CNN. By changing the decoder's
dilation architecture, we control the effective context from previously
generated words. In experiments, we find that there is a trade off between the
contextual capacity of the decoder and the amount of encoding information used.
We show that with the right decoder, VAE can outperform LSTM language models.
We demonstrate perplexity gains on two datasets, representing the first
positive experimental result on the use VAE for generative modeling of text.
Further, we conduct an in-depth investigation of the use of VAE (with our new
decoding architecture) for semi-supervised and unsupervised labeling tasks,
demonstrating gains over several strong baselines. | Improved Variational Autoencoders for Text Modeling using Dilated
Convolutions | 2,017 | http://arxiv.org/pdf/1702.08139v2 | Title Improved Variational Autoencoders Text Modeling using Dilated Convolutions Summary Recent work generative modeling text found variational autoencoders VAE incorporating LSTM decoder perform worse simpler LSTM language model Bowman et al 2015 negative result far poorly understood attributed propensity LSTM decoder ignore conditioning information encoder paper experiment new type decoder VAE dilated CNN changing decoder dilation architecture control effective context previously generated word experiment find trade contextual capacity decoder amount encoding information used show right decoder VAE outperform LSTM language model demonstrate perplexity gain two datasets representing first positive experimental result use VAE generative modeling text conduct indepth investigation use VAE new decoding architecture semisupervised unsupervised labeling task demonstrating gain several strong baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.031139275059103966, 0.08634331822395325, -0.017654934898018837, 0.04814178869128227, -0.019941547885537148, 0.008397197350859642, 0.024982603266835213, -0.0009738968801684678, -0.06876417994499207, -0.0027986273635178804, -0.01723877340555191, -0.04735134541988373, -0.016995685175061226, 0.08497116714715958, 0.029974274337291718, -0.06995140016078949, 0.008329715579748154, 0.00674284715205431, -0.005609680898487568, -0.03268841654062271, 0.016469867900013924, -0.004548820201307535, 0.007043746300041676, 0.017710164189338684, 0.005214586853981018, 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632 | 632 | ['Hairong Liu', 'Zhenyao Zhu', 'Xiangang Li', 'Sanjeev Satheesh'] | 1703.00096v2 | Most existing sequence labelling models rely on a fixed decomposition of a
target sequence into a sequence of basic units. These methods suffer from two
major drawbacks: 1) the set of basic units is fixed, such as the set of words,
characters or phonemes in speech recognition, and 2) the decomposition of
target sequences is fixed. These drawbacks usually result in sub-optimal
performance of modeling sequences. In this pa- per, we extend the popular CTC
loss criterion to alleviate these limitations, and propose a new loss function
called Gram-CTC. While preserving the advantages of CTC, Gram-CTC automatically
learns the best set of basic units (grams), as well as the most suitable
decomposition of tar- get sequences. Unlike CTC, Gram-CTC allows the model to
output variable number of characters at each time step, which enables the model
to capture longer term dependency and improves the computational efficiency. We
demonstrate that the proposed Gram-CTC improves CTC in terms of both
performance and efficiency on the large vocabulary speech recognition task at
multiple scales of data, and that with Gram-CTC we can outperform the
state-of-the-art on a standard speech benchmark. | Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence
Labelling | 2,017 | http://arxiv.org/pdf/1703.00096v2 | Title GramCTC Automatic Unit Selection Target Decomposition Sequence Labelling Summary existing sequence labelling model rely fixed decomposition target sequence sequence basic unit method suffer two major drawback 1 set basic unit fixed set word character phoneme speech recognition 2 decomposition target sequence fixed drawback usually result suboptimal performance modeling sequence pa per extend popular CTC loss criterion alleviate limitation propose new loss function called GramCTC preserving advantage CTC GramCTC automatically learns best set basic unit gram well suitable decomposition tar get sequence Unlike CTC GramCTC allows model output variable number character time step enables model capture longer term dependency improves computational efficiency demonstrate proposed GramCTC improves CTC term performance efficiency large vocabulary speech recognition task multiple scale data GramCTC outperform stateoftheart standard speech benchmark Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.029830938205122948, 0.04454585537314415, 0.034033939242362976, 0.027482159435749054, -0.016133543103933334, 0.0429430790245533, 0.019833918660879135, 0.0166368018835783, -0.020888321101665497, -0.02717079408466816, -0.000637555553112179, -0.029659390449523926, 0.0503210611641407, 0.0027869197074323893, -0.012183088809251785, -0.03943726047873497, 0.013752523809671402, -0.019913941621780396, -0.021907595917582512, 0.03690272197127342, 0.007233833894133568, 0.016051357612013817, -0.03217029944062233, 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633 | 633 | ['Govardana Sachithanandam Ramachandran', 'Ajay Sohmshetty'] | 1703.03939v1 | We examine Memory Networks for the task of question answering (QA), under
common real world scenario where training examples are scarce and under weakly
supervised scenario, that is only extrinsic labels are available for training.
We propose extensions for the Dynamic Memory Network (DMN), specifically within
the attention mechanism, we call the resulting Neural Architecture as Dynamic
Memory Tensor Network (DMTN). Ultimately, we see that our proposed extensions
results in over 80% improvement in the number of task passed against the
baselined standard DMN and 20% more task passed compared to state-of-the-art
End-to-End Memory Network for Facebook's single task weakly trained 1K bAbi
dataset. | Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model) | 2,017 | http://arxiv.org/pdf/1703.03939v1 | Title Ask Even Dynamic Memory Tensor Networks Extended Model Summary examine Memory Networks task question answering QA common real world scenario training example scarce weakly supervised scenario extrinsic label available training propose extension Dynamic Memory Network DMN specifically within attention mechanism call resulting Neural Architecture Dynamic Memory Tensor Network DMTN Ultimately see proposed extension result 80 improvement number task passed baselined standard DMN 20 task passed compared stateoftheart EndtoEnd Memory Network Facebooks single task weakly trained 1K bAbi dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01754993386566639, -0.01755429059267044, -0.037438519299030304, 0.00472089322283864, -0.007093632128089666, 0.03203025832772255, 0.011884788051247597, -0.020065026357769966, -0.010369201190769672, -0.016145899891853333, -0.0012571010738611221, -0.04139208793640137, -0.013332019560039043, 0.0303955115377903, 0.04763966426253319, -0.0967307910323143, 0.01506795920431614, -0.02696680836379528, 0.008611982688307762, 0.002184249460697174, -0.0076439217664301395, -0.012440175749361515, -0.010187981650233269, -0.004347670823335648, -0.023720620200037956, -0.014595185406506062, -0.041439373046159744, 0.009979501366615295, 0.052272725850343704, -0.024827448651194572, 0.03530227765440941, 0.05046798661351204, 0.03586718067526817, -0.013045492582023144, 2.1525797819776926e-06, -0.010809888131916523, 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634 | 634 | ['Lior Fritz', 'David Burshtein'] | 1703.10356v2 | A simplified speech recognition system that uses the maximum mutual
information (MMI) criterion is considered. End-to-end training using gradient
descent is suggested, similarly to the training of connectionist temporal
classification (CTC). We use an MMI criterion with a simple language model in
the training stage, and a standard HMM decoder. Our method compares favorably
to CTC in terms of performance, robustness, decoding time, disk footprint and
quality of alignments. The good alignments enable the use of a straightforward
ensemble method, obtained by simply averaging the predictions of several neural
network models, that were trained separately end-to-end. The ensemble method
yields a considerable reduction in the word error rate. | Simplified End-to-End MMI Training and Voting for ASR | 2,017 | http://arxiv.org/pdf/1703.10356v2 | Title Simplified EndtoEnd MMI Training Voting ASR Summary simplified speech recognition system us maximum mutual information MMI criterion considered Endtoend training using gradient descent suggested similarly training connectionist temporal classification CTC use MMI criterion simple language model training stage standard HMM decoder method compare favorably CTC term performance robustness decoding time disk footprint quality alignment good alignment enable use straightforward ensemble method obtained simply averaging prediction several neural network model trained separately endtoend ensemble method yield considerable reduction word error rate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.026127465069293976, 0.040844596922397614, 0.02768903411924839, 0.027729712426662445, 0.0038226251490414143, 0.022259080782532692, 0.006353217642754316, -0.0174814872443676, -0.0220170971006155, -0.013572930358350277, -0.042083434760570526, -0.01985441893339157, 0.07458272576332092, 0.010344461537897587, -0.018603907898068428, -0.029162060469388962, 0.030020106583833694, 0.0035121890250593424, -0.025505851954221725, -0.017866773530840874, 0.017608750611543655, 0.011661186814308167, -0.0655086562037468, 0.05291207879781723, -0.014391973614692688, 0.0008889735327102244, -0.018549617379903793, -0.057586148381233215, -0.014362383633852005, -0.05204789340496063, -0.003844739403575659, 0.04056219756603241, 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635 | 635 | ['Alec Radford', 'Rafal Jozefowicz', 'Ilya Sutskever'] | 1704.01444v2 | We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment analysis. These representations, learned in an unsupervised
manner, achieve state of the art on the binary subset of the Stanford Sentiment
Treebank. They are also very data efficient. When using only a handful of
labeled examples, our approach matches the performance of strong baselines
trained on full datasets. We also demonstrate the sentiment unit has a direct
influence on the generative process of the model. Simply fixing its value to be
positive or negative generates samples with the corresponding positive or
negative sentiment. | Learning to Generate Reviews and Discovering Sentiment | 2,017 | http://arxiv.org/pdf/1704.01444v2 | Title Learning Generate Reviews Discovering Sentiment Summary explore property bytelevel recurrent language model given sufficient amount capacity training data compute time representation learned model include disentangled feature corresponding highlevel concept Specifically find single unit performs sentiment analysis representation learned unsupervised manner achieve state art binary subset Stanford Sentiment Treebank also data efficient using handful labeled example approach match performance strong baseline trained full datasets also demonstrate sentiment unit direct influence generative process model Simply fixing value positive negative generates sample corresponding positive negative sentiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04611093923449516, 0.05558440461754799, -0.005580800119787455, 0.02697698585689068, -0.04319903999567032, 0.011704379692673683, -0.022924331948161125, 0.0033520604483783245, -0.012462098151445389, -0.06253533810377121, 0.004261876456439495, -0.01009831391274929, -0.013715391047298908, 0.09788276255130768, -0.023839350789785385, -0.033560965210199356, 0.028832318261265755, 0.05521506071090698, 0.03502810001373291, -0.011933837085962296, 0.017634710296988487, 0.02746134251356125, 0.023842578753829002, 0.006851478014141321, -0.005904749967157841, 0.0038663458544760942, 0.01734299398958683, -0.006789231672883034, -0.039065878838300705, -0.048155527561903, 0.033038828521966934, 0.03453065827488899, 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636 | 636 | ['Marek Rei'] | 1704.07156v1 | We propose a sequence labeling framework with a secondary training objective,
learning to predict surrounding words for every word in the dataset. This
language modeling objective incentivises the system to learn general-purpose
patterns of semantic and syntactic composition, which are also useful for
improving accuracy on different sequence labeling tasks. The architecture was
evaluated on a range of datasets, covering the tasks of error detection in
learner texts, named entity recognition, chunking and POS-tagging. The novel
language modeling objective provided consistent performance improvements on
every benchmark, without requiring any additional annotated or unannotated
data. | Semi-supervised Multitask Learning for Sequence Labeling | 2,017 | http://arxiv.org/pdf/1704.07156v1 | Title Semisupervised Multitask Learning Sequence Labeling Summary propose sequence labeling framework secondary training objective learning predict surrounding word every word dataset language modeling objective incentivises system learn generalpurpose pattern semantic syntactic composition also useful improving accuracy different sequence labeling task architecture evaluated range datasets covering task error detection learner text named entity recognition chunking POStagging novel language modeling objective provided consistent performance improvement every benchmark without requiring additional annotated unannotated data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05621160566806793, -0.00344303366728127, 0.021400338038802147, 0.00940660759806633, -0.03424806892871857, 0.039948977530002594, -0.0034205468837171793, -0.040986593812704086, 0.03295162692666054, -0.06236645206809044, -0.036111000925302505, -0.030893437564373016, -0.014165043830871582, 0.058400969952344894, 0.01943737082183361, -0.052354954183101654, 0.039066560566425323, 0.020366022363305092, 0.04352416843175888, 0.07139609754085541, 0.02075180970132351, 0.01671265996992588, -0.03536085784435272, 0.02953844889998436, -0.033540431410074234, 0.008906759321689606, -0.009975653141736984, -0.04188281670212746, 0.01716105453670025, -0.069365955889225, 0.046424489468336105, 0.0683969035744667, 0.016252215951681137, 0.02558234892785549, 2.1479572751559317e-06, -0.027288099750876427, 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637 | 637 | ['Danhao Zhu', 'Si Shen', 'Xin-Yu Dai', 'Jiajun Chen'] | 1705.01346v1 | Recurrent Neural Network (RNN) has been widely applied for sequence modeling.
In RNN, the hidden states at current step are full connected to those at
previous step, thus the influence from less related features at previous step
may potentially decrease model's learning ability. We propose a simple
technique called parallel cells (PCs) to enhance the learning ability of
Recurrent Neural Network (RNN). In each layer, we run multiple small RNN cells
rather than one single large cell. In this paper, we evaluate PCs on 2 tasks.
On language modeling task on PTB (Penn Tree Bank), our model outperforms state
of art models by decreasing perplexity from 78.6 to 75.3. On Chinese-English
translation task, our model increases BLEU score for 0.39 points than baseline
model. | Going Wider: Recurrent Neural Network With Parallel Cells | 2,017 | http://arxiv.org/pdf/1705.01346v1 | Title Going Wider Recurrent Neural Network Parallel Cells Summary Recurrent Neural Network RNN widely applied sequence modeling RNN hidden state current step full connected previous step thus influence le related feature previous step may potentially decrease model learning ability propose simple technique called parallel cell PCs enhance learning ability Recurrent Neural Network RNN layer run multiple small RNN cell rather one single large cell paper evaluate PCs 2 task language modeling task PTB Penn Tree Bank model outperforms state art model decreasing perplexity 786 753 ChineseEnglish translation task model increase BLEU score 039 point baseline model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.009379123337566853, 0.025332294404506683, -0.024564344435930252, 0.029763156548142433, -0.04892450571060181, 0.014327235519886017, 0.052719030529260635, -0.021398676559329033, -0.05949936434626579, -0.007525046821683645, 0.010927753522992134, -0.06231151521205902, 0.04505552724003792, 0.01007845439016819, 0.02437165006995201, -0.04486626759171486, 0.05186132341623306, 0.011581246741116047, 0.0022229759488254786, -0.006073246710002422, 0.0030552346725016832, -0.004447569139301777, 0.005545635707676411, 0.05057355761528015, 0.005529013462364674, -0.039320237934589386, -0.019162919372320175, -0.01830454170703888, -0.01222191657871008, -0.08996676653623581, -0.0035455545876175165, 0.04542246833443642, 0.028490278869867325, 0.07118690013885498, 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638 | 638 | ['Zhiyuan Tang', 'Dong Wang', 'Yixiang Chen', 'Lantian Li', 'Andrew Abel'] | 1705.03151v3 | Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions. | Phonetic Temporal Neural Model for Language Identification | 2,017 | http://arxiv.org/pdf/1705.03151v3 | Title Phonetic Temporal Neural Model Language Identification Summary Deep neural model particularly LSTMRNN model shown great potential language identification LID However use phonetic information largely overlooked existing neural LID method although information used successfully conventional phonetic LID system present phonetic temporal neural model LID LSTMRNN LID system accepts phonetic feature produced phonediscriminative DNN input rather raw acoustic feature new model similar traditional phonetic LID method phonetic knowledge much richer frame level involves compacted information phone experiment conducted Babel database AP16OLR database demonstrate temporal phonetic neural approach effective significantly outperforms existing acoustic neural model also outperforms conventional ivector approach short utterance noisy condition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.010538937523961067, 0.051746804267168045, 0.009194403886795044, 0.02748473547399044, -0.019157063215970993, 0.004291570279747248, 0.04313242807984352, -0.024848293513059616, -0.01615096442401409, 0.013568012043833733, -0.02703378163278103, -0.058014191687107086, 0.07893027365207672, 9.135649634117726e-06, 0.017817990854382515, -0.010379272513091564, 0.010510406456887722, -0.02412528544664383, 0.04761709272861481, -0.020860640332102776, 0.01400195062160492, -0.002854152349755168, -0.06147179380059242, 0.039242781698703766, -0.0011090937769040465, 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639 | 639 | ['Hamed Zamani', 'W. Bruce Croft'] | 1705.03556v2 | Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objective in well-known word embedding algorithms, e.g., word2vec, is to
accurately predict adjacent word(s) for a given word or context. However, this
objective is not necessarily equivalent to the goal of many information
retrieval (IR) tasks. The primary objective in various IR tasks is to capture
relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word
embedding models that learn word representations based on query-document
relevance information. In this paper, we propose two learning models with
different objective functions; one learns a relevance distribution over the
vocabulary set for each query, and the other classifies each term as belonging
to the relevant or non-relevant class for each query. To train our models, we
used over six million unique queries and the top ranked documents retrieved in
response to each query, which are assumed to be relevant to the query. We
extrinsically evaluate our learned word representation models using two IR
tasks: query expansion and query classification. Both query expansion
experiments on four TREC collections and query classification experiments on
the KDD Cup 2005 dataset suggest that the relevance-based word embedding models
significantly outperform state-of-the-art proximity-based embedding models,
such as word2vec and GloVe. | Relevance-based Word Embedding | 2,017 | http://arxiv.org/pdf/1705.03556v2 | Title Relevancebased Word Embedding Summary Learning highdimensional dense representation vocabulary term also known word embedding recently attracted much attention natural language processing information retrieval task embedding vector typically learned based term proximity large corpus mean objective wellknown word embedding algorithm eg word2vec accurately predict adjacent word given word context However objective necessarily equivalent goal many information retrieval IR task primary objective various IR task capture relevance instead term proximity syntactic even semantic similarity motivation developing unsupervised relevancebased word embedding model learn word representation based querydocument relevance information paper propose two learning model different objective function one learns relevance distribution vocabulary set query classifies term belonging relevant nonrelevant class query train model used six million unique query top ranked document retrieved response query assumed relevant query extrinsically evaluate learned word representation model using two IR task query expansion query classification query expansion experiment four TREC collection query classification experiment KDD Cup 2005 dataset suggest relevancebased word embedding model significantly outperform stateoftheart proximitybased embedding model word2vec GloVe Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.055417437106370926, 0.007513559889048338, 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640 | 640 | ['Tao Lei', 'Wengong Jin', 'Regina Barzilay', 'Tommi Jaakkola'] | 1705.09037v3 | The design of neural architectures for structured objects is typically guided
by experimental insights rather than a formal process. In this work, we appeal
to kernels over combinatorial structures, such as sequences and graphs, to
derive appropriate neural operations. We introduce a class of deep recurrent
neural operations and formally characterize their associated kernel spaces. Our
recurrent modules compare the input to virtual reference objects (cf. filters
in CNN) via the kernels. Similar to traditional neural operations, these
reference objects are parameterized and directly optimized in end-to-end
training. We empirically evaluate the proposed class of neural architectures on
standard applications such as language modeling and molecular graph regression,
achieving state-of-the-art results across these applications. | Deriving Neural Architectures from Sequence and Graph Kernels | 2,017 | http://arxiv.org/pdf/1705.09037v3 | Title Deriving Neural Architectures Sequence Graph Kernels Summary design neural architecture structured object typically guided experimental insight rather formal process work appeal kernel combinatorial structure sequence graph derive appropriate neural operation introduce class deep recurrent neural operation formally characterize associated kernel space recurrent module compare input virtual reference object cf filter CNN via kernel Similar traditional neural operation reference object parameterized directly optimized endtoend training empirically evaluate proposed class neural architecture standard application language modeling molecular graph regression achieving stateoftheart result across application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.014733297750353813, 0.04007579758763313, -0.02269957773387432, 0.011624796316027641, -0.007425493560731411, -0.0345165878534317, 0.004953464493155479, 0.03830841928720474, 0.024719465523958206, 0.001643794821575284, 0.05051158741116524, -0.012354633770883083, -0.002367529785260558, 0.09574262797832489, 0.03041026182472706, 0.009375378489494324, 0.01775435358285904, 0.013290539383888245, -0.024228984490036964, -0.02653355710208416, 0.02356850728392601, -0.0071320789866149426, 0.03764327988028526, 0.027376465499401093, -0.03788759559392929, 0.006301058456301689, 0.014251671731472015, -0.017857136204838753, -0.032383840531110764, 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641 | 641 | ['Michał Zapotoczny', 'Paweł Rychlikowski', 'Jan Chorowski'] | 1705.10209v1 | We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistically relevant concepts can be inferred from word
spellings. We analyze the representations of characters and words that are
learned by the network to establish which properties of languages were
accounted for. In particular we show that the parser has approximately learned
to associate Latin characters with their Cyrillic counterparts and that it can
group Polish and Russian words that have a similar grammatical function.
Finally, we evaluate the parser on selected languages from the Universal
Dependencies dataset and show that it is competitive with other recently
proposed state-of-the art methods, while having a simple structure. | On Multilingual Training of Neural Dependency Parsers | 2,017 | http://arxiv.org/pdf/1705.10209v1 | Title Multilingual Training Neural Dependency Parsers Summary show recently proposed neural dependency parser improved joint training multiple language family parser implemented deep neural network whose input orthographic representation word order successfully parse network discover linguistically relevant concept inferred word spelling analyze representation character word learned network establish property language accounted particular show parser approximately learned associate Latin character Cyrillic counterpart group Polish Russian word similar grammatical function Finally evaluate parser selected language Universal Dependencies dataset show competitive recently proposed stateofthe art method simple structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01944381557404995, 0.052187707275152206, 0.013219852931797504, 0.08311734348535538, -0.046065762639045715, 0.02014671266078949, 0.0015334389172494411, -0.010520074516534805, -0.010884782299399376, -0.013641287572681904, 0.007354326080530882, -0.037044864147901535, 0.016053467988967896, -0.0013310664799064398, 0.008907009847462177, -0.06741923838853836, 0.005824755411595106, 0.0059600044041872025, -0.023764073848724365, 0.006599707528948784, 0.035204727202653885, 0.024933306500315666, -0.027640653774142265, 0.019578205421566963, -0.04759589582681656, -0.00891587883234024, 0.02804730273783207, -0.0009466815390624106, 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642 | 642 | ['Marian Tietz', 'Tayfun Alpay', 'Johannes Twiefel', 'Stefan Wermter'] | 1706.02124v2 | Ladder networks are a notable new concept in the field of semi-supervised
learning by showing state-of-the-art results in image recognition tasks while
being compatible with many existing neural architectures. We present the
recurrent ladder network, a novel modification of the ladder network, for
semi-supervised learning of recurrent neural networks which we evaluate with a
phoneme recognition task on the TIMIT corpus. Our results show that the model
is able to consistently outperform the baseline and achieve fully-supervised
baseline performance with only 75% of all labels which demonstrates that the
model is capable of using unsupervised data as an effective regulariser. | Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks | 2,017 | http://arxiv.org/pdf/1706.02124v2 | Title SemiSupervised Phoneme Recognition Recurrent Ladder Networks Summary Ladder network notable new concept field semisupervised learning showing stateoftheart result image recognition task compatible many existing neural architecture present recurrent ladder network novel modification ladder network semisupervised learning recurrent neural network evaluate phoneme recognition task TIMIT corpus result show model able consistently outperform baseline achieve fullysupervised baseline performance 75 label demonstrates model capable using unsupervised data effective regulariser Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.008167542517185211, 0.0533309131860733, 0.02403194271028042, 0.020374435931444168, 0.018757058307528496, -0.005465107969939709, 0.037153396755456924, -0.020483996719121933, 0.0333324559032917, 0.033537209033966064, -0.05731246620416641, -0.034057050943374634, 0.019655730575323105, 0.0464630089700222, 0.018118418753147125, -0.0799979642033577, -0.0025356162805110216, 0.003036073874682188, 0.014822909608483315, -0.010807536542415619, 0.002677864395081997, -0.011338485404849052, -0.03964697942137718, 0.007254239171743393, -0.05642402917146683, 0.015051816590130329, 0.006903624627739191, 0.0096899950876832, -0.03621714934706688, -0.047479309141635895, 0.008180445991456509, 0.03895394131541252, 0.004199220333248377, 0.0839885026216507, 2.3201628209790215e-06, 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643 | 643 | ['Junbo', 'Zhao', 'Yoon Kim', 'Kelly Zhang', 'Alexander M. Rush', 'Yann LeCun'] | 1706.04223v2 | While autoencoders are a key technique in representation learning for
continuous structures, such as images or wave forms, developing general-purpose
autoencoders for discrete structures, such as text sequence or discretized
images, has proven to be more challenging. In particular, discrete inputs make
it more difficult to learn a smooth encoder that preserves the complex local
relationships in the input space. In this work, we propose an adversarially
regularized autoencoder (ARAE) with the goal of learning more robust
discrete-space representations. ARAE jointly trains both a rich discrete-space
encoder, such as an RNN, and a simpler continuous space generator function,
while using generative adversarial network (GAN) training to constrain the
distributions to be similar. This method yields a smoother contracted code
space that maps similar inputs to nearby codes, and also an implicit latent
variable GAN model for generation. Experiments on text and discretized images
demonstrate that the GAN model produces clean interpolations and captures the
multimodality of the original space, and that the autoencoder produces improve-
ments in semi-supervised learning as well as state-of-the-art results in
unaligned text style transfer task using only a shared continuous-space
representation. | Adversarially Regularized Autoencoders | 2,017 | http://arxiv.org/pdf/1706.04223v2 | Title Adversarially Regularized Autoencoders Summary autoencoders key technique representation learning continuous structure image wave form developing generalpurpose autoencoders discrete structure text sequence discretized image proven challenging particular discrete input make difficult learn smooth encoder preserve complex local relationship input space work propose adversarially regularized autoencoder ARAE goal learning robust discretespace representation ARAE jointly train rich discretespace encoder RNN simpler continuous space generator function using generative adversarial network GAN training constrain distribution similar method yield smoother contracted code space map similar input nearby code also implicit latent variable GAN model generation Experiments text discretized image demonstrate GAN model produce clean interpolation capture multimodality original space autoencoder produce improve ments semisupervised learning well stateoftheart result unaligned text style transfer task using shared continuousspace representation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.003998361993581057, 0.10605769604444504, -0.023995626717805862, 0.047150611877441406, 0.011567512527108192, -0.005254093091934919, 0.02776133269071579, 0.010005134157836437, -0.03373934328556061, 0.008464936167001724, -0.037195928394794464, 0.001652729930356145, -0.002006764989346266, 0.05625103786587715, 0.0657661110162735, -0.09693030267953873, -0.010911847464740276, 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644 | 644 | ['Marek Rei', 'Helen Yannakoudakis'] | 1707.05227v1 | We investigate the utility of different auxiliary objectives and training
strategies within a neural sequence labeling approach to error detection in
learner writing. Auxiliary costs provide the model with additional linguistic
information, allowing it to learn general-purpose compositional features that
can then be exploited for other objectives. Our experiments show that a joint
learning approach trained with parallel labels on in-domain data improves
performance over the previous best error detection system. While the resulting
model has the same number of parameters, the additional objectives allow it to
be optimised more efficiently and achieve better performance. | Auxiliary Objectives for Neural Error Detection Models | 2,017 | http://arxiv.org/pdf/1707.05227v1 | Title Auxiliary Objectives Neural Error Detection Models Summary investigate utility different auxiliary objective training strategy within neural sequence labeling approach error detection learner writing Auxiliary cost provide model additional linguistic information allowing learn generalpurpose compositional feature exploited objective experiment show joint learning approach trained parallel label indomain data improves performance previous best error detection system resulting model number parameter additional objective allow optimised efficiently achieve better performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.053201671689748764, 0.03163161873817444, 0.020694365724921227, 0.008173013105988503, 0.015069123357534409, 0.03954949602484703, 0.004487816710025072, 0.004229953978210688, -0.010595637373626232, -0.049160826951265335, 0.0034147666301578283, -0.036453258246183395, 0.029932990670204163, 0.0006174460286274552, 0.005157488863915205, -0.01047626230865717, 0.06456518173217773, 0.02054833061993122, 0.07033975422382355, 0.01739024557173252, 0.001876484602689743, -0.029372865334153175, -0.016014203429222107, 0.03145461902022362, -0.0896243155002594, 0.011422066017985344, -0.015706168487668037, -0.0087082264944911, -0.00409860210493207, -0.07641302794218063, 0.07432547956705093, 0.04553740844130516, -0.004294905345886946, 0.07898368686437607, 2.0938309717166703e-06, 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645 | 645 | ['Youmna Farag', 'Marek Rei', 'Ted Briscoe'] | 1707.06841v1 | We propose a novel word embedding pre-training approach that exploits writing
errors in learners' scripts. We compare our method to previous models that tune
the embeddings based on script scores and the discrimination between correct
and corrupt word contexts in addition to the generic commonly-used embeddings
pre-trained on large corpora. The comparison is achieved by using the
aforementioned models to bootstrap a neural network that learns to predict a
holistic score for scripts. Furthermore, we investigate augmenting our model
with error corrections and monitor the impact on performance. Our results show
that our error-oriented approach outperforms other comparable ones which is
further demonstrated when training on more data. Additionally, extending the
model with corrections provides further performance gains when data sparsity is
an issue. | An Error-Oriented Approach to Word Embedding Pre-Training | 2,017 | http://arxiv.org/pdf/1707.06841v1 | Title ErrorOriented Approach Word Embedding PreTraining Summary propose novel word embedding pretraining approach exploit writing error learner script compare method previous model tune embeddings based script score discrimination correct corrupt word context addition generic commonlyused embeddings pretrained large corpus comparison achieved using aforementioned model bootstrap neural network learns predict holistic score script Furthermore investigate augmenting model error correction monitor impact performance result show errororiented approach outperforms comparable one demonstrated training data Additionally extending model correction provides performance gain data sparsity issue Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.009240753017365932, 0.028996504843235016, 0.02859247289597988, 0.041179753839969635, 0.01521011721342802, 0.008262046612799168, -0.022857774049043655, 0.0456257164478302, 0.02617158554494381, -0.0855959951877594, -0.03694905713200569, -0.012926281429827213, 0.02177080698311329, 0.04429576173424721, 0.03244822472333908, -0.005561791360378265, 0.04159867763519287, 0.04831725358963013, -0.00410118093714118, 0.014294748194515705, 0.030215244740247726, 0.03474147617816925, 0.029350535944104195, 0.03736894577741623, -0.04771669954061508, 0.012220156379044056, 0.023319996893405914, 0.008878177031874657, 0.01714104413986206, -0.05885319784283638, 0.03271478787064552, 0.06615793704986572, 0.05524684116244316, 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646 | 646 | ['Kartik Goyal', 'Graham Neubig', 'Chris Dyer', 'Taylor Berg-Kirkpatrick'] | 1708.00111v2 | Beam search is a desirable choice of test-time decoding algorithm for neural
sequence models because it potentially avoids search errors made by simpler
greedy methods. However, typical cross entropy training procedures for these
models do not directly consider the behaviour of the final decoding method. As
a result, for cross-entropy trained models, beam decoding can sometimes yield
reduced test performance when compared with greedy decoding. In order to train
models that can more effectively make use of beam search, we propose a new
training procedure that focuses on the final loss metric (e.g. Hamming loss)
evaluated on the output of beam search. While well-defined, this "direct loss"
objective is itself discontinuous and thus difficult to optimize. Hence, in our
approach, we form a sub-differentiable surrogate objective by introducing a
novel continuous approximation of the beam search decoding procedure. In
experiments, we show that optimizing this new training objective yields
substantially better results on two sequence tasks (Named Entity Recognition
and CCG Supertagging) when compared with both cross entropy trained greedy
decoding and cross entropy trained beam decoding baselines. | A Continuous Relaxation of Beam Search for End-to-end Training of Neural
Sequence Models | 2,017 | http://arxiv.org/pdf/1708.00111v2 | Title Continuous Relaxation Beam Search Endtoend Training Neural Sequence Models Summary Beam search desirable choice testtime decoding algorithm neural sequence model potentially avoids search error made simpler greedy method However typical cross entropy training procedure model directly consider behaviour final decoding method result crossentropy trained model beam decoding sometimes yield reduced test performance compared greedy decoding order train model effectively make use beam search propose new training procedure focus final loss metric eg Hamming loss evaluated output beam search welldefined direct loss objective discontinuous thus difficult optimize Hence approach form subdifferentiable surrogate objective introducing novel continuous approximation beam search decoding procedure experiment show optimizing new training objective yield substantially better result two sequence task Named Entity Recognition CCG Supertagging compared cross entropy trained greedy decoding cross entropy trained beam decoding baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03741150721907616, 0.030471503734588623, 0.03220384195446968, 0.025864267721772194, 0.0011311826528981328, 0.03016337752342224, -0.016953421756625175, 0.013072589412331581, 0.004709469620138407, 0.01821279339492321, -0.028442755341529846, -0.03348269686102867, 0.00608800770714879, -0.007798672188073397, -0.009676429443061352, -0.052162881940603256, 0.05853249877691269, 0.017096830531954765, 0.0262697022408247, 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647 | 647 | ['Stephen Merity', 'Nitish Shirish Keskar', 'Richard Socher'] | 1708.02182v1 | Recurrent neural networks (RNNs), such as long short-term memory networks
(LSTMs), serve as a fundamental building block for many sequence learning
tasks, including machine translation, language modeling, and question
answering. In this paper, we consider the specific problem of word-level
language modeling and investigate strategies for regularizing and optimizing
LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on
hidden-to-hidden weights as a form of recurrent regularization. Further, we
introduce NT-ASGD, a variant of the averaged stochastic gradient method,
wherein the averaging trigger is determined using a non-monotonic condition as
opposed to being tuned by the user. Using these and other regularization
strategies, we achieve state-of-the-art word level perplexities on two data
sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the
effectiveness of a neural cache in conjunction with our proposed model, we
achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and
52.0 on WikiText-2. | Regularizing and Optimizing LSTM Language Models | 2,017 | http://arxiv.org/pdf/1708.02182v1 | Title Regularizing Optimizing LSTM Language Models Summary Recurrent neural network RNNs long shortterm memory network LSTMs serve fundamental building block many sequence learning task including machine translation language modeling question answering paper consider specific problem wordlevel language modeling investigate strategy regularizing optimizing LSTMbased model propose weightdropped LSTM us DropConnect hiddentohidden weight form recurrent regularization introduce NTASGD variant averaged stochastic gradient method wherein averaging trigger determined using nonmonotonic condition opposed tuned user Using regularization strategy achieve stateoftheart word level perplexity two data set 573 Penn Treebank 658 WikiText2 exploring effectiveness neural cache conjunction proposed model achieve even lower stateoftheart perplexity 528 Penn Treebank 520 WikiText2 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03623424842953682, 0.055790528655052185, 0.004418463911861181, 0.06507393717765808, -0.03167427331209183, -0.003000459400936961, 0.013029449619352818, 0.016414618119597435, -0.02476254291832447, -0.019145308062434196, -0.03168972209095955, -0.07381616532802582, 0.05584363266825676, 0.011357031762599945, 0.006464107893407345, -0.05264847353100777, 0.03119942732155323, 0.0034407218918204308, -0.01738889329135418, -0.0015936113195493817, 0.023708553984761238, -0.01693808101117611, 0.034965626895427704, 0.03739149495959282, 0.019810790196061134, 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648 | 648 | ['DeLiang Wang', 'Jitong Chen'] | 1708.07524v1 | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background noise are learned from training data. Over the past
decade, many supervised separation algorithms have been put forward. In
particular, the recent introduction of deep learning to supervised speech
separation has dramatically accelerated progress and boosted separation
performance. This article provides a comprehensive overview of the research on
deep learning based supervised speech separation in the last several years. We
first introduce the background of speech separation and the formulation of
supervised separation. Then we discuss three main components of supervised
separation: learning machines, training targets, and acoustic features. Much of
the overview is on separation algorithms where we review monaural methods,
including speech enhancement (speech-nonspeech separation), speaker separation
(multi-talker separation), and speech dereverberation, as well as
multi-microphone techniques. The important issue of generalization, unique to
supervised learning, is discussed. This overview provides a historical
perspective on how advances are made. In addition, we discuss a number of
conceptual issues, including what constitutes the target source. | Supervised Speech Separation Based on Deep Learning: An Overview | 2,017 | http://arxiv.org/pdf/1708.07524v1 | Title Supervised Speech Separation Based Deep Learning Overview Summary Speech separation task separating target speech background interference Traditionally speech separation studied signal processing problem recent approach formulates speech separation supervised learning problem discriminative pattern speech speaker background noise learned training data past decade many supervised separation algorithm put forward particular recent introduction deep learning supervised speech separation dramatically accelerated progress boosted separation performance article provides comprehensive overview research deep learning based supervised speech separation last several year first introduce background speech separation formulation supervised separation discus three main component supervised separation learning machine training target acoustic feature Much overview separation algorithm review monaural method including speech enhancement speechnonspeech separation speaker separation multitalker separation speech dereverberation well multimicrophone technique important issue generalization unique supervised learning discussed overview provides historical perspective advance made addition discus number conceptual issue including constitutes target source Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.038107819855213165, 0.01861475221812725, 0.027826201170682907, 0.046899646520614624, 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649 | 649 | ['Marek Rei', 'Luana Bulat', 'Douwe Kiela', 'Ekaterina Shutova'] | 1709.00575v1 | The ubiquity of metaphor in our everyday communication makes it an important
problem for natural language understanding. Yet, the majority of metaphor
processing systems to date rely on hand-engineered features and there is still
no consensus in the field as to which features are optimal for this task. In
this paper, we present the first deep learning architecture designed to capture
metaphorical composition. Our results demonstrate that it outperforms the
existing approaches in the metaphor identification task. | Grasping the Finer Point: A Supervised Similarity Network for Metaphor
Detection | 2,017 | http://arxiv.org/pdf/1709.00575v1 | Title Grasping Finer Point Supervised Similarity Network Metaphor Detection Summary ubiquity metaphor everyday communication make important problem natural language understanding Yet majority metaphor processing system date rely handengineered feature still consensus field feature optimal task paper present first deep learning architecture designed capture metaphorical composition result demonstrate outperforms existing approach metaphor identification task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05495205149054527, 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650 | 650 | ['Danushka Bollegala', 'Kohei Hayashi', 'Ken-ichi Kawarabayashi'] | 1709.06671v1 | Distributed word embeddings have shown superior performances in numerous
Natural Language Processing (NLP) tasks. However, their performances vary
significantly across different tasks, implying that the word embeddings learnt
by those methods capture complementary aspects of lexical semantics. Therefore,
we believe that it is important to combine the existing word embeddings to
produce more accurate and complete \emph{meta-embeddings} of words. For this
purpose, we propose an unsupervised locally linear meta-embedding learning
method that takes pre-trained word embeddings as the input, and produces more
accurate meta embeddings. Unlike previously proposed meta-embedding learning
methods that learn a global projection over all words in a vocabulary, our
proposed method is sensitive to the differences in local neighbourhoods of the
individual source word embeddings. Moreover, we show that vector concatenation,
a previously proposed highly competitive baseline approach for integrating word
embeddings, can be derived as a special case of the proposed method.
Experimental results on semantic similarity, word analogy, relation
classification, and short-text classification tasks show that our
meta-embeddings to significantly outperform prior methods in several benchmark
datasets, establishing a new state of the art for meta-embeddings. | Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words | 2,017 | http://arxiv.org/pdf/1709.06671v1 | Title Think Globally Embed Locally Locally Linear Metaembedding Words Summary Distributed word embeddings shown superior performance numerous Natural Language Processing NLP task However performance vary significantly across different task implying word embeddings learnt method capture complementary aspect lexical semantics Therefore believe important combine existing word embeddings produce accurate complete emphmetaembeddings word purpose propose unsupervised locally linear metaembedding learning method take pretrained word embeddings input produce accurate meta embeddings Unlike previously proposed metaembedding learning method learn global projection word vocabulary proposed method sensitive difference local neighbourhood individual source word embeddings Moreover show vector concatenation previously proposed highly competitive baseline approach integrating word embeddings derived special case proposed method Experimental result semantic similarity word analogy relation classification shorttext classification task show metaembeddings significantly outperform prior method several benchmark datasets establishing new state art metaembeddings Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012094358913600445, 0.01215886790305376, 0.007214866112917662, 0.071959488093853, -0.008110486902296543, -0.003673032158985734, 0.007556939963251352, -0.031710900366306305, 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651 | 651 | ['Bin Bi', 'Hao Ma'] | 1709.09749v1 | Previous studies have demonstrated the empirical success of word embeddings
in various applications. In this paper, we investigate the problem of learning
distributed representations for text documents which many machine learning
algorithms take as input for a number of NLP tasks.
We propose a neural network model, KeyVec, which learns document
representations with the goal of preserving key semantics of the input text. It
enables the learned low-dimensional vectors to retain the topics and important
information from the documents that will flow to downstream tasks. Our
empirical evaluations show the superior quality of KeyVec representations in
two different document understanding tasks. | KeyVec: Key-semantics Preserving Document Representations | 2,017 | http://arxiv.org/pdf/1709.09749v1 | Title KeyVec Keysemantics Preserving Document Representations Summary Previous study demonstrated empirical success word embeddings various application paper investigate problem learning distributed representation text document many machine learning algorithm take input number NLP task propose neural network model KeyVec learns document representation goal preserving key semantics input text enables learned lowdimensional vector retain topic important information document flow downstream task empirical evaluation show superior quality KeyVec representation two different document understanding task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04206375405192375, -0.0009373161592520773, 0.021352212876081467, 0.03221099078655243, -0.022413088008761406, 0.015619372017681599, -0.0132781146094203, 0.039899419993162155, -0.010965495370328426, -0.0836675688624382, 0.008697493001818657, 0.010289817117154598, -0.008964930661022663, 0.0367455892264843, 0.0169996228069067, -0.04665815457701683, 0.06096040830016136, 0.018946092575788498, -0.028824424371123314, -0.0025549819692969322, 0.020083874464035034, 0.02290051430463791, 0.02128179371356964, 0.025916898623108864, -0.01457150001078844, -0.05768032371997833, -0.046986520290374756, -0.044817060232162476, -0.02462063357234001, 0.006525038275867701, -0.0047007957473397255, 0.004472361411899328, 0.03450607508420944, -0.0012963158078491688, 2.022685748670483e-06, 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652 | 652 | ['Shuai Tang', 'Hailin Jin', 'Chen Fang', 'Zhaowen Wang', 'Virginia R. de Sa'] | 1710.10380v2 | Context information plays an important role in human language understanding,
and it is also useful for machines to learn vector representations of language.
In this paper, we explore an asymmetric encoder-decoder structure for
unsupervised context-based sentence representation learning. As a result, we
build an encoder-decoder architecture with an RNN encoder and a CNN decoder. We
further combine a suite of effective designs to significantly improve model
efficiency while also achieving better performance. Our model is trained on two
different large unlabeled corpora, and in both cases transferability is
evaluated on a set of downstream language understanding tasks. We empirically
show that our model is simple and fast while producing rich sentence
representations that excel in downstream tasks. | Exploring Asymmetric Encoder-Decoder Structure for Context-based
Sentence Representation Learning | 2,017 | http://arxiv.org/pdf/1710.10380v2 | Title Exploring Asymmetric EncoderDecoder Structure Contextbased Sentence Representation Learning Summary Context information play important role human language understanding also useful machine learn vector representation language paper explore asymmetric encoderdecoder structure unsupervised contextbased sentence representation learning result build encoderdecoder architecture RNN encoder CNN decoder combine suite effective design significantly improve model efficiency also achieving better performance model trained two different large unlabeled corpus case transferability evaluated set downstream language understanding task empirically show model simple fast producing rich sentence representation excel downstream task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.031040281057357788, 0.012019283138215542, -0.020153719931840897, 0.10539435595273972, -0.011638340540230274, 0.0379156768321991, 0.016123764216899872, -0.021891169250011444, -0.071658194065094, -0.08328808844089508, -0.021733807399868965, -0.017984628677368164, -0.016857003793120384, 0.02559058926999569, -0.009070992469787598, -0.052885863929986954, 0.016558922827243805, 0.0006990280235186219, 0.0052687725983560085, -0.0011254975106567144, 0.0477183572947979, 0.010371913202106953, 0.007536375895142555, 0.04041743651032448, 0.01008067186921835, -0.0069245705381035805, -0.005994732957333326, 0.017464203760027885, 0.004165902268141508, -0.02893676981329918, 0.05466657131910324, 0.0349360853433609, 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653 | 653 | ['Qiming Chen', 'Ren Wu'] | 1712.09662v1 | The Convolution Neural Network (CNN) has demonstrated the unique advantage in
audio, image and text learning; recently it has also challenged Recurrent
Neural Networks (RNNs) with long short-term memory cells (LSTM) in
sequence-to-sequence learning, since the computations involved in CNN are
easily parallelizable whereas those involved in RNN are mostly sequential,
leading to a performance bottleneck. However, unlike RNN, the native CNN lacks
the history sensitivity required for sequence transformation; therefore
enhancing the sequential order awareness, or position-sensitivity, becomes the
key to make CNN the general deep learning model. In this work we introduce an
extended CNN model with strengthen position-sensitivity, called PoseNet. A
notable feature of PoseNet is the asymmetric treatment of position information
in the encoder and the decoder. Experiments shows that PoseNet allows us to
improve the accuracy of CNN based sequence-to-sequence learning significantly,
achieving around 33-36 BLEU scores on the WMT 2014 English-to-German
translation task, and around 44-46 BLEU scores on the English-to-French
translation task. | CNN Is All You Need | 2,017 | http://arxiv.org/pdf/1712.09662v1 | Title CNN Need Summary Convolution Neural Network CNN demonstrated unique advantage audio image text learning recently also challenged Recurrent Neural Networks RNNs long shortterm memory cell LSTM sequencetosequence learning since computation involved CNN easily parallelizable whereas involved RNN mostly sequential leading performance bottleneck However unlike RNN native CNN lack history sensitivity required sequence transformation therefore enhancing sequential order awareness positionsensitivity becomes key make CNN general deep learning model work introduce extended CNN model strengthen positionsensitivity called PoseNet notable feature PoseNet asymmetric treatment position information encoder decoder Experiments show PoseNet allows u improve accuracy CNN based sequencetosequence learning significantly achieving around 3336 BLEU score WMT 2014 EnglishtoGerman translation task around 4446 BLEU score EnglishtoFrench translation task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.023207545280456543, 0.055911850184202194, -0.016193347051739693, 0.03825205937027931, 0.010565239936113358, -0.011192799545824528, 0.031835008412599564, 0.025277020409703255, -0.055737681686878204, -0.008467582054436207, -0.018891112878918648, -0.024584462866187096, 0.026755500584840775, -0.0033087453339248896, 0.010294949635863304, -0.06995046883821487, 0.00495947478339076, 0.036176834255456924, -0.015096744522452354, 0.0012970060342922807, -0.011441408656537533, -0.013236022554337978, 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654 | 654 | ['Tim Rocktäschel'] | 1712.09687v1 | The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for most preprocessing steps and task-specific
assumptions. However, in many cases representation learning requires a large
amount of annotated training data to generalize well to unseen data. Such
labeled training data is provided by human annotators who often use formal
logic as the language for specifying annotations. This thesis investigates
different combinations of representation learning methods with logic for
reducing the need for annotated training data, and for improving
generalization. | Combining Representation Learning with Logic for Language Processing | 2,017 | http://arxiv.org/pdf/1712.09687v1 | Title Combining Representation Learning Logic Language Processing Summary current stateoftheart many natural language processing automated knowledge base completion task held representation learning method learn distributed vector representation symbol via gradientbased optimization require little handcrafted feature thus avoiding need preprocessing step taskspecific assumption However many case representation learning requires large amount annotated training data generalize well unseen data labeled training data provided human annotator often use formal logic language specifying annotation thesis investigates different combination representation learning method logic reducing need annotated training data improving generalization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04245384782552719, -0.010935433208942413, -0.00979213509708643, 0.019917266443371773, -0.03041921928524971, 0.02238231897354126, -0.009313097223639488, 0.012592816725373268, 0.06660055369138718, -0.09160167723894119, 0.018299521878361702, 0.04960286617279053, 0.0038200649432837963, 0.05390718951821327, 0.012001699768006802, -0.0878070667386055, 0.0390266552567482, 0.04444265365600586, 0.02872171811759472, 0.014048948884010315, 0.030047113075852394, 0.023747825995087624, 0.0043891966342926025, 0.04384391009807587, -0.017339464277029037, -0.013452853076159954, 0.012074800208210945, -0.04796392098069191, 0.0016177804209291935, -0.01871483586728573, 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655 | 655 | ['Dietmar Volz'] | 1308.1603v4 | It will be shown that according to theorems of K. Menger, every neuron grid
if identified with a curve is able to preserve the adopted qualitative
structure of a data space. Furthermore, if this identification is made, the
neuron grid structure can always be mapped to a subset of a universal neuron
grid which is constructable in three space dimensions. Conclusions will be
drawn for established neuron grid types as well as neural fields. | A Note on Topology Preservation in Classification, and the Construction
of a Universal Neuron Grid | 2,013 | http://arxiv.org/pdf/1308.1603v4 | Title Note Topology Preservation Classification Construction Universal Neuron Grid Summary shown according theorem K Menger every neuron grid identified curve able preserve adopted qualitative structure data space Furthermore identification made neuron grid structure always mapped subset universal neuron grid constructable three space dimension Conclusions drawn established neuron grid type well neural field Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.018376406282186508, 0.009212622418999672, 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656 | 656 | ['Louis Yuanlong Shao'] | 1210.8442v3 | One conjecture in both deep learning and classical connectionist viewpoint is
that the biological brain implements certain kinds of deep networks as its
back-end. However, to our knowledge, a detailed correspondence has not yet been
set up, which is important if we want to bridge between neuroscience and
machine learning. Recent researches emphasized the biological plausibility of
Linear-Nonlinear-Poisson (LNP) neuron model. We show that with neurally
plausible settings, the whole network is capable of representing any Boltzmann
machine and performing a semi-stochastic Bayesian inference algorithm lying
between Gibbs sampling and variational inference. | Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On
Boltzmann Machines | 2,012 | http://arxiv.org/pdf/1210.8442v3 | Title LinearNonlinearPoisson Neuron Networks Perform Bayesian Inference Boltzmann Machines Summary One conjecture deep learning classical connectionist viewpoint biological brain implement certain kind deep network backend However knowledge detailed correspondence yet set important want bridge neuroscience machine learning Recent research emphasized biological plausibility LinearNonlinearPoisson LNP neuron model show neurally plausible setting whole network capable representing Boltzmann machine performing semistochastic Bayesian inference algorithm lying Gibbs sampling variational inference Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04433208703994751, 0.0404491052031517, -0.029001904651522636, 0.02259504236280918, -0.025148529559373856, 0.007500724866986275, 0.043871693313121796, -0.01455585565418005, -0.03772580251097679, -0.005837182514369488, -0.030823180451989174, 0.01564974896609783, 0.004207680933177471, 0.05848691985011101, 0.0803089365363121, -0.020722821354866028, -0.019703267142176628, 0.007237229496240616, -0.0013636249350383878, -0.05285084992647171, -0.002536566462367773, -0.019920306280255318, -0.05486674606800079, 0.005044419784098864, 0.018038688227534294, 0.01056581549346447, 0.02077222242951393, -0.04539313167333603, 0.017737042158842087, 0.04712957888841629, -0.045354846864938736, -0.02221488766372204, -0.00792655348777771, -0.0056810565292835236, 1.8560266425993177e-06, -0.01750905252993107, 0.024729585275053978, -0.01769948936998844, -0.0029595140367746353, 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657 | 657 | ['Enmei Tu', 'Nikola Kasabov', 'Jie Yang'] | 1603.05594v1 | This paper proposes a new method for an optimized mapping of temporal
variables, describing a temporal stream data, into the recently proposed
NeuCube spiking neural network architecture. This optimized mapping extends the
use of the NeuCube, which was initially designed for spatiotemporal brain data,
to work on arbitrary stream data and to achieve a better accuracy of temporal
pattern recognition, a better and earlier event prediction and a better
understanding of complex temporal stream data through visualization of the
NeuCube connectivity. The effect of the new mapping is demonstrated on three
bench mark problems. The first one is early prediction of patient sleep stage
event from temporal physiological data. The second one is pattern recognition
of dynamic temporal patterns of traffic in the Bay Area of California and the
last one is the Challenge 2012 contest data set. In all cases the use of the
proposed mapping leads to an improved accuracy of pattern recognition and event
prediction and a better understanding of the data when compared to traditional
machine learning techniques or spiking neural network reservoirs with arbitrary
mapping of the variables. | Mapping Temporal Variables into the NeuCube for Improved Pattern
Recognition, Predictive Modelling and Understanding of Stream Data | 2,016 | http://arxiv.org/pdf/1603.05594v1 | Title Mapping Temporal Variables NeuCube Improved Pattern Recognition Predictive Modelling Understanding Stream Data Summary paper proposes new method optimized mapping temporal variable describing temporal stream data recently proposed NeuCube spiking neural network architecture optimized mapping extends use NeuCube initially designed spatiotemporal brain data work arbitrary stream data achieve better accuracy temporal pattern recognition better earlier event prediction better understanding complex temporal stream data visualization NeuCube connectivity effect new mapping demonstrated three bench mark problem first one early prediction patient sleep stage event temporal physiological data second one pattern recognition dynamic temporal pattern traffic Bay Area California last one Challenge 2012 contest data set case use proposed mapping lead improved accuracy pattern recognition event prediction better understanding data compared traditional machine learning technique spiking neural network reservoir arbitrary mapping variable Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03617202118039131, -0.012301030568778515, -0.032941706478595734, -0.005760688800364733, 0.004749706480652094, 0.016142960637807846, 0.019739855080842972, -0.02388072945177555, -0.021417731419205666, 0.01587310992181301, 0.03027486428618431, -0.020441768690943718, 0.025372151285409927, 0.1118316650390625, 0.029465314000844955, -0.05357062444090843, 0.04698621481657028, 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658 | 658 | ['Jörn Hees', 'Rouven Bauer', 'Joachim Folz', 'Damian Borth', 'Andreas Dengel'] | 1607.07249v3 | Efficient usage of the knowledge provided by the Linked Data community is
often hindered by the need for domain experts to formulate the right SPARQL
queries to answer questions. For new questions they have to decide which
datasets are suitable and in which terminology and modelling style to phrase
the SPARQL query.
In this work we present an evolutionary algorithm to help with this
challenging task. Given a training list of source-target node-pair examples our
algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The
learned patterns can be visualised to form the basis for further investigation,
or they can be used to predict target nodes for new source nodes.
Amongst others, we apply our algorithm to a dataset of several hundred human
associations (such as "circle - square") to find patterns for them in DBpedia.
We show the scalability of the algorithm by running it against a SPARQL
endpoint loaded with > 7.9 billion triples. Further, we use the resulting
SPARQL queries to mimic human associations with a Mean Average Precision (MAP)
of 39.9 % and a Recall@10 of 63.9 %. | An Evolutionary Algorithm to Learn SPARQL Queries for
Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia | 2,016 | http://arxiv.org/pdf/1607.07249v3 | Title Evolutionary Algorithm Learn SPARQL Queries SourceTargetPairs Finding Patterns Human Associations DBpedia Summary Efficient usage knowledge provided Linked Data community often hindered need domain expert formulate right SPARQL query answer question new question decide datasets suitable terminology modelling style phrase SPARQL query work present evolutionary algorithm help challenging task Given training list sourcetarget nodepair example algorithm learn pattern SPARQL query SPARQL endpoint learned pattern visualised form basis investigation used predict target node new source node Amongst others apply algorithm dataset several hundred human association circle square find pattern DBpedia show scalability algorithm running SPARQL endpoint loaded 79 billion triple use resulting SPARQL query mimic human association Mean Average Precision MAP 399 Recall10 639 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05303521826863289, 0.0714755430817604, -0.023572899401187897, -0.0002667703665792942, 0.0017019326332956553, -0.007072402164340019, 0.00022472212731372565, 0.00924236886203289, 0.014618105255067348, -0.013973248191177845, 0.010560072027146816, 0.06168920919299126, -0.004193740896880627, 0.08381201326847076, -0.006917405873537064, -0.007000985089689493, 0.026194240897893906, 0.03719426691532135, -0.031228482723236084, 0.009213998913764954, 0.016036102548241615, 0.03854157030582428, 0.01250026561319828, -0.018678192049264908, 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659 | 659 | ['Anthony L. Caterini', 'Dong Eui Chang'] | 1608.04374v2 | In this paper, a geometric framework for neural networks is proposed. This
framework uses the inner product space structure underlying the parameter set
to perform gradient descent not in a component-based form, but in a
coordinate-free manner. Convolutional neural networks are described in this
framework in a compact form, with the gradients of standard --- and
higher-order --- loss functions calculated for each layer of the network. This
approach can be applied to other network structures and provides a basis on
which to create new networks. | A Geometric Framework for Convolutional Neural Networks | 2,016 | http://arxiv.org/pdf/1608.04374v2 | Title Geometric Framework Convolutional Neural Networks Summary paper geometric framework neural network proposed framework us inner product space structure underlying parameter set perform gradient descent componentbased form coordinatefree manner Convolutional neural network described framework compact form gradient standard higherorder loss function calculated layer network approach applied network structure provides basis create new network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben 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660 | 660 | ['Anthony Caterini', 'Dong Eui Chang'] | 1610.01549v2 | Deep Neural Networks (DNNs) have become very popular for prediction in many
areas. Their strength is in representation with a high number of parameters
that are commonly learned via gradient descent or similar optimization methods.
However, the representation is non-standardized, and the gradient calculation
methods are often performed using component-based approaches that break
parameters down into scalar units, instead of considering the parameters as
whole entities. In this work, these problems are addressed. Standard notation
is used to represent DNNs in a compact framework. Gradients of DNN loss
functions are calculated directly over the inner product space on which the
parameters are defined. This framework is general and is applied to two common
network types: the Multilayer Perceptron and the Deep Autoencoder. | A Novel Representation of Neural Networks | 2,016 | http://arxiv.org/pdf/1610.01549v2 | Title Novel Representation Neural Networks Summary Deep Neural Networks DNNs become popular prediction many area strength representation high number parameter commonly learned via gradient descent similar optimization method However representation nonstandardized gradient calculation method often performed using componentbased approach break parameter scalar unit instead considering parameter whole entity work problem addressed Standard notation used represent DNNs compact framework Gradients DNN loss function calculated directly inner product space parameter defined framework general applied two common network type Multilayer Perceptron Deep Autoencoder Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04897966608405113, 0.018185332417488098, -0.04835548624396324, 0.03892781585454941, 0.01695767045021057, -0.028132939711213112, 0.06393872946500778, -0.034992609173059464, -0.01274050772190094, -0.003143599722534418, -0.04479924961924553, -0.010154358111321926, 0.009778140112757683, 0.01962733082473278, 0.026690669357776642, -0.06988711655139923, -0.006819447036832571, 0.03607569634914398, -0.0420084223151207, -0.018033383414149284, 0.02333003096282482, -0.008422343991696835, 0.029220547527074814, 0.0475727915763855, -0.07167615741491318, 0.03198855742812157, 0.007483326364308596, -0.02586699090898037, 0.021586386486887932, -0.029994284734129906, 0.04165743291378021, -0.003164447145536542, 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661 | 661 | ['Ardavan Salehi Nobandegani', 'Thomas R. Shultz'] | 1701.05004v1 | Humans are not only adept in recognizing what class an input instance belongs
to (i.e., classification task), but perhaps more remarkably, they can imagine
(i.e., generate) plausible instances of a desired class with ease, when
prompted. Inspired by this, we propose a framework which allows transforming
Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative
models, thereby enabling CCNNs to generate samples from a category of interest.
CCNNs are a well-known class of deterministic, discriminative NNs, which
autonomously construct their topology, and have been successful in giving
accounts for a variety of psychological phenomena. Our proposed framework is
based on a Markov Chain Monte Carlo (MCMC) method, called the
Metropolis-adjusted Langevin algorithm, which capitalizes on the gradient
information of the target distribution to direct its explorations towards
regions of high probability, thereby achieving good mixing properties. Through
extensive simulations, we demonstrate the efficacy of our proposed framework. | Converting Cascade-Correlation Neural Nets into Probabilistic Generative
Models | 2,017 | http://arxiv.org/pdf/1701.05004v1 | Title Converting CascadeCorrelation Neural Nets Probabilistic Generative Models Summary Humans adept recognizing class input instance belongs ie classification task perhaps remarkably imagine ie generate plausible instance desired class ease prompted Inspired propose framework allows transforming CascadeCorrelation Neural Networks CCNNs probabilistic generative model thereby enabling CCNNs generate sample category interest CCNNs wellknown class deterministic discriminative NNs autonomously construct topology successful giving account variety psychological phenomenon proposed framework based Markov Chain Monte Carlo MCMC method called Metropolisadjusted Langevin algorithm capitalizes gradient information target distribution direct exploration towards region high probability thereby achieving good mixing property extensive simulation demonstrate efficacy proposed framework Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00014280565665103495, 0.09656462073326111, -0.032682426273822784, 0.012405757792294025, -0.03379938006401062, 0.002445181366056204, 0.053696729242801666, -0.02378443256020546, -0.009187174960970879, 0.014752513729035854, -0.0019390084780752659, 0.034141041338443756, 0.009422731585800648, 0.023686226457357407, -0.011698721908032894, -0.0854855477809906, 0.01972358301281929, 0.008159610442817211, -0.04826315492391586, -0.002359313890337944, -0.003393653780221939, -0.01748698763549328, -0.021164070814847946, 0.028511829674243927, -0.05368562042713165, -0.026272952556610107, 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662 | 662 | ['Ludvig Ericson', 'Rendani Mbuvha'] | 1701.05130v1 | Artificial Neural Networks (ANNs) have received increasing attention in
recent years with applications that span a wide range of disciplines including
vital domains such as medicine, network security and autonomous transportation.
However, neural network architectures are becoming increasingly complex and
with an increasing need to obtain real-time results from such models, it has
become pivotal to use parallelization as a mechanism for speeding up network
training and deployment. In this work we propose an implementation of Network
Parallel Training through Cannon's Algorithm for matrix multiplication. We show
that increasing the number of processes speeds up training until the point
where process communication costs become prohibitive; this point varies by
network complexity. We also show through empirical efficiency calculations that
the speedup obtained is superlinear. | On the Performance of Network Parallel Training in Artificial Neural
Networks | 2,017 | http://arxiv.org/pdf/1701.05130v1 | Title Performance Network Parallel Training Artificial Neural Networks Summary Artificial Neural Networks ANNs received increasing attention recent year application span wide range discipline including vital domain medicine network security autonomous transportation However neural network architecture becoming increasingly complex increasing need obtain realtime result model become pivotal use parallelization mechanism speeding network training deployment work propose implementation Network Parallel Training Cannons Algorithm matrix multiplication show increasing number process speed training point process communication cost become prohibitive point varies network complexity also show empirical efficiency calculation speedup obtained superlinear Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00465263519436121, -0.006094884127378464, -0.03394026309251785, 0.025037458166480064, 0.006173739209771156, 0.01080020610243082, 0.08450014889240265, -0.028840018436312675, -0.03439641743898392, -0.01235903799533844, -0.002023539040237665, 0.02701936475932598, 0.008542477153241634, -0.019386006519198418, 0.023082798346877098, -0.012857578694820404, 0.023524314165115356, 0.039177488535642624, 0.006207180675119162, -0.035279568284749985, 0.03117060661315918, -0.007178247440606356, -0.055672597140073776, 0.05279412493109703, -0.04134548082947731, 0.02183009311556816, 0.0068088374100625515, -0.03935588151216507, 0.040403109043836594, -0.055058181285858154, 0.03862244263291359, -0.039504535496234894, 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663 | 663 | ['Misha Denil', 'Sergio Gómez Colmenarejo', 'Serkan Cabi', 'David Saxton', 'Nando de Freitas'] | 1706.06383v1 | We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop disentangled interpretable representations that allow them to
generalize to a wide variety of zero-shot semantic tasks. | Programmable Agents | 2,017 | http://arxiv.org/pdf/1706.06383v1 | Title Programmable Agents Summary build deep RL agent execute declarative program expressed formal language agent learn ground term language environment generalize behavior test time execute new program refer object referenced training agent develop disentangled interpretable representation allow generalize wide variety zeroshot semantic task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.015608362853527069, 0.04042176902294159, -0.008841241709887981, 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664 | 664 | ['Wojciech Samek', 'Thomas Wiegand', 'Klaus-Robert Müller'] | 1708.08296v1 | With the availability of large databases and recent improvements in deep
learning methodology, the performance of AI systems is reaching or even
exceeding the human level on an increasing number of complex tasks. Impressive
examples of this development can be found in domains such as image
classification, sentiment analysis, speech understanding or strategic game
playing. However, because of their nested non-linear structure, these highly
successful machine learning and artificial intelligence models are usually
applied in a black box manner, i.e., no information is provided about what
exactly makes them arrive at their predictions. Since this lack of transparency
can be a major drawback, e.g., in medical applications, the development of
methods for visualizing, explaining and interpreting deep learning models has
recently attracted increasing attention. This paper summarizes recent
developments in this field and makes a plea for more interpretability in
artificial intelligence. Furthermore, it presents two approaches to explaining
predictions of deep learning models, one method which computes the sensitivity
of the prediction with respect to changes in the input and one approach which
meaningfully decomposes the decision in terms of the input variables. These
methods are evaluated on three classification tasks. | Explainable Artificial Intelligence: Understanding, Visualizing and
Interpreting Deep Learning Models | 2,017 | http://arxiv.org/pdf/1708.08296v1 | Title Explainable Artificial Intelligence Understanding Visualizing Interpreting Deep Learning Models Summary availability large database recent improvement deep learning methodology performance AI system reaching even exceeding human level increasing number complex task Impressive example development found domain image classification sentiment analysis speech understanding strategic game playing However nested nonlinear structure highly successful machine learning artificial intelligence model usually applied black box manner ie information provided exactly make arrive prediction Since lack transparency major drawback eg medical application development method visualizing explaining interpreting deep learning model recently attracted increasing attention paper summarizes recent development field make plea interpretability artificial intelligence Furthermore present two approach explaining prediction deep learning model one method computes sensitivity prediction respect change input one approach meaningfully decomposes decision term input variable method evaluated three classification task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.004354760050773621, 0.025785479694604874, -0.040497828274965286, 0.008426116779446602, 0.0018225862877443433, 0.011091730557382107, 0.017602263018488884, 0.015021657571196556, -0.03170483559370041, 0.016350071877241135, 0.003045437391847372, 0.02138860709965229, -0.0071899862959980965, 0.0925000011920929, 0.01675647497177124, -0.07915414124727249, 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665 | 665 | ['Maithra Raghu', 'Alex Irpan', 'Jacob Andreas', 'Robert Kleinberg', 'Quoc V. Le', 'Jon Kleinberg'] | 1711.02301v3 | Deep reinforcement learning has achieved many recent successes, but our
understanding of its strengths and limitations is hampered by the lack of rich
environments in which we can fully characterize optimal behavior, and
correspondingly diagnose individual actions against such a characterization.
Here we consider a family of combinatorial games, arising from work of Erdos,
Selfridge, and Spencer, and we propose their use as environments for evaluating
and comparing different approaches to reinforcement learning. These games have
a number of appealing features: they are challenging for current learning
approaches, but they form (i) a low-dimensional, simply parametrized
environment where (ii) there is a linear closed form solution for optimal
behavior from any state, and (iii) the difficulty of the game can be tuned by
changing environment parameters in an interpretable way. We use these
Erdos-Selfridge-Spencer games not only to compare different algorithms, but
test for generalization, make comparisons to supervised learning, analyse
multiagent play, and even develop a self play algorithm. | Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? | 2,017 | http://arxiv.org/pdf/1711.02301v3 | Title Deep Reinforcement Learning Solve ErdosSelfridgeSpencer Games Summary Deep reinforcement learning achieved many recent success understanding strength limitation hampered lack rich environment fully characterize optimal behavior correspondingly diagnose individual action characterization consider family combinatorial game arising work Erdos Selfridge Spencer propose use environment evaluating comparing different approach reinforcement learning game number appealing feature challenging current learning approach form lowdimensional simply parametrized environment ii linear closed form solution optimal behavior state iii difficulty game tuned changing environment parameter interpretable way use ErdosSelfridgeSpencer game compare different algorithm test generalization make comparison supervised learning analyse multiagent play even develop self play algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00046241149539127946, 0.012712793424725533, -0.039370663464069366, 0.024848148226737976, -0.035569336265325546, -0.009846172295510769, 0.011720039881765842, 0.0038367644883692265, -0.031720489263534546, 0.046587537974119186, 0.00973449181765318, 0.033039599657058716, -0.055030446499586105, 0.05365367978811264, 0.004801297560334206, -0.026107098907232285, 0.015109318308532238, -0.01615874469280243, 0.013515086844563484, -0.01908649131655693, 0.006724271457642317, -0.04591314494609833, 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666 | 666 | ['Christopher J. Cueva', 'Xue-Xin Wei'] | 1803.07770v1 | Decades of research on the neural code underlying spatial navigation have
revealed a diverse set of neural response properties. The Entorhinal Cortex
(EC) of the mammalian brain contains a rich set of spatial correlates,
including grid cells which encode space using tessellating patterns. However,
the mechanisms and functional significance of these spatial representations
remain largely mysterious. As a new way to understand these neural
representations, we trained recurrent neural networks (RNNs) to perform
navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find
that grid-like spatial response patterns emerge in trained networks, along with
units that exhibit other spatial correlates, including border cells and
band-like cells. All these different functional types of neurons have been
observed experimentally. The order of the emergence of grid-like and border
cells is also consistent with observations from developmental studies.
Together, our results suggest that grid cells, border cells and others as
observed in EC may be a natural solution for representing space efficiently
given the predominant recurrent connections in the neural circuits. | Emergence of grid-like representations by training recurrent neural
networks to perform spatial localization | 2,018 | http://arxiv.org/pdf/1803.07770v1 | Title Emergence gridlike representation training recurrent neural network perform spatial localization Summary Decades research neural code underlying spatial navigation revealed diverse set neural response property Entorhinal Cortex EC mammalian brain contains rich set spatial correlate including grid cell encode space using tessellating pattern However mechanism functional significance spatial representation remain largely mysterious new way understand neural representation trained recurrent neural network RNNs perform navigation task 2D arena based velocity input Surprisingly find gridlike spatial response pattern emerge trained network along unit exhibit spatial correlate including border cell bandlike cell different functional type neuron observed experimentally order emergence gridlike border cell also consistent observation developmental study Together result suggest grid cell border cell others observed EC may natural solution representing space efficiently given predominant recurrent connection neural circuit Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0009491996024735272, 0.018944118171930313, -0.03762098774313927, -0.005847285035997629, 0.01585310883820057, 0.039605412632226944, 0.015460417605936527, -0.018404824659228325, -0.020391466096043587, 0.03385598212480545, -0.029813308268785477, -0.029472600668668747, 0.022981125861406326, 0.05065745860338211, 0.08331341296434402, 0.012007235549390316, -0.0067926496267318726, 0.012850956059992313, 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667 | 667 | ['Dasika Ratna Deepthi', 'Sujeet Kuchibhotla', 'K. Eswaran'] | 0712.0932v1 | In this paper, we present a Mirroring Neural Network architecture to perform
non-linear dimensionality reduction and Object Recognition using a reduced
lowdimensional characteristic vector. In addition to dimensionality reduction,
the network also reconstructs (mirrors) the original high-dimensional input
vector from the reduced low-dimensional data. The Mirroring Neural Network
architecture has more number of processing elements (adalines) in the outer
layers and the least number of elements in the central layer to form a
converging-diverging shape in its configuration. Since this network is able to
reconstruct the original image from the output of the innermost layer (which
contains all the information about the input pattern), these outputs can be
used as object signature to classify patterns. The network is trained to
minimize the discrepancy between actual output and the input by back
propagating the mean squared error from the output layer to the input layer.
After successfully training the network, it can reduce the dimension of input
vectors and mirror the patterns fed to it. The Mirroring Neural Network
architecture gave very good results on various test patterns. | Dimensionality Reduction and Reconstruction using Mirroring Neural
Networks and Object Recognition based on Reduced Dimension Characteristic
Vector | 2,007 | http://arxiv.org/pdf/0712.0932v1 | Title Dimensionality Reduction Reconstruction using Mirroring Neural Networks Object Recognition based Reduced Dimension Characteristic Vector Summary paper present Mirroring Neural Network architecture perform nonlinear dimensionality reduction Object Recognition using reduced lowdimensional characteristic vector addition dimensionality reduction network also reconstructs mirror original highdimensional input vector reduced lowdimensional data Mirroring Neural Network architecture number processing element adalines outer layer least number element central layer form convergingdiverging shape configuration Since network able reconstruct original image output innermost layer contains information input pattern output used object signature classify pattern network trained minimize discrepancy actual output input back propagating mean squared error output layer input layer successfully training network reduce dimension input vector mirror pattern fed Mirroring Neural Network architecture gave good result various test pattern Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.024565834552049637, 0.008700142614543438, -0.027781834825873375, 0.06486279517412186, -0.012282852083444595, 0.009073599241673946, 0.050775375217199326, 0.015378891490399837, -0.05731024965643883, 0.012595757842063904, -0.0018333224579691887, 0.008624824695289135, 0.029290223494172096, 0.02354268915951252, 0.03453419730067253, -0.07182392477989197, -0.012179616838693619, 0.07027961313724518, -0.08042944967746735, -0.051331162452697754, 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668 | 668 | ['Yongnan Ji', 'Pierre-Yves Herve', 'Uwe Aickelin', 'Alain Pitiot'] | 1004.3708v1 | Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI)
data based on a standard General Linear Model (GLM)and spectral clustering was
recently proposed as a means to alleviate the issues associated with spatial
normalization in fMRI. However, for all its appeal, a GLM-based parcellation
approach introduces its own biases, in the form of a priori knowledge about the
shape of Hemodynamic Response Function (HRF) and task-related signal changes,
or about the subject behaviour during the task. In this paper, we introduce a
data-driven version of the spectral clustering parcellation, based on
Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of
the GLM. First, a number of independent components are automatically selected.
Seed voxels are then obtained from the associated ICA maps and we compute the
PLS latent variables between the fMRI signal of the seed voxels (which covers
regional variations of the HRF) and the principal components of the signal
across all voxels. Finally, we parcellate all subjects data with a spectral
clustering of the PLS latent variables. We present results of the application
of the proposed method on both single-subject and multi-subject fMRI datasets.
Preliminary experimental results, evaluated with intra-parcel variance of GLM
t-values and PLS derived t-values, indicate that this data-driven approach
offers improvement in terms of parcellation accuracy over GLM based techniques. | Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach | 2,010 | http://arxiv.org/pdf/1004.3708v1 | Title Parcellation fMRI Datasets ICA PLSA Data Driven Approach Summary Intersubject parcellation functional Magnetic Resonance Imaging fMRI data based standard General Linear Model GLMand spectral clustering recently proposed mean alleviate issue associated spatial normalization fMRI However appeal GLMbased parcellation approach introduces bias form priori knowledge shape Hemodynamic Response Function HRF taskrelated signal change subject behaviour task paper introduce datadriven version spectral clustering parcellation based Independent Component Analysis ICA Partial Least Squares PLS instead GLM First number independent component automatically selected Seed voxels obtained associated ICA map compute PLS latent variable fMRI signal seed voxels cover regional variation HRF principal component signal across voxels Finally parcellate subject data spectral clustering PLS latent variable present result application proposed method singlesubject multisubject fMRI datasets Preliminary experimental result evaluated intraparcel variance GLM tvalues PLS derived tvalues indicate datadriven approach offer improvement term parcellation accuracy GLM based technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.00980097334831953, 0.022604599595069885, -0.043833956122398376, -0.005876052659004927, 0.012391098774969578, 0.04289724677801132, 0.06515447795391083, 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669 | 669 | ['N. Popescu-Bodorin', 'V. E. Balas', 'I. M. Motoc'] | 1110.6483v2 | The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognition tests. It is shown here that the use of proposed neural
network support leads to an improvement in the artificial perception of the
separation between the intra- and inter-class score distributions by moving
them away from each other. | Iris Codes Classification Using Discriminant and Witness Directions | 2,011 | http://arxiv.org/pdf/1110.6483v2 | Title Iris Codes Classification Using Discriminant Witness Directions Summary main topic discussed paper use intelligence biometric decision defuzzification neural training model proposed tested possible solution dealing natural fuzzification appears intra interclass distribution score computed iris recognition test shown use proposed neural network support lead improvement artificial perception separation intra interclass score distribution moving away Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen 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670 | 670 | ['Harris Georgiou'] | 0910.3348v1 | Medical Informatics and the application of modern signal processing in the
assistance of the diagnostic process in medical imaging is one of the more
recent and active research areas today. This thesis addresses a variety of
issues related to the general problem of medical image analysis, specifically
in mammography, and presents a series of algorithms and design approaches for
all the intermediate levels of a modern system for computer-aided diagnosis
(CAD). The diagnostic problem is analyzed with a systematic approach, first
defining the imaging characteristics and features that are relevant to probable
pathology in mammo-grams. Next, these features are quantified and fused into
new, integrated radio-logical systems that exhibit embedded digital signal
processing, in order to improve the final result and minimize the radiological
dose for the patient. In a higher level, special algorithms are designed for
detecting and encoding these clinically interest-ing imaging features, in order
to be used as input to advanced pattern classifiers and machine learning
models. Finally, these approaches are extended in multi-classifier models under
the scope of Game Theory and optimum collective deci-sion, in order to produce
efficient solutions for combining classifiers with minimum computational costs
for advanced diagnostic systems. The material covered in this thesis is related
to a total of 18 published papers, 6 in scientific journals and 12 in
international conferences. | Algorithms for Image Analysis and Combination of Pattern Classifiers
with Application to Medical Diagnosis | 2,009 | http://arxiv.org/pdf/0910.3348v1 | Title Algorithms Image Analysis Combination Pattern Classifiers Application Medical Diagnosis Summary Medical Informatics application modern signal processing assistance diagnostic process medical imaging one recent active research area today thesis address variety issue related general problem medical image analysis specifically mammography present series algorithm design approach intermediate level modern system computeraided diagnosis CAD diagnostic problem analyzed systematic approach first defining imaging characteristic feature relevant probable pathology mammogram Next feature quantified fused new integrated radiological system exhibit embedded digital signal processing order improve final result minimize radiological dose patient higher level special algorithm designed detecting encoding clinically interesting imaging feature order used input advanced pattern classifier machine learning model Finally approach extended multiclassifier model scope Game Theory optimum collective decision order produce efficient solution combining classifier minimum computational cost advanced diagnostic system material covered thesis related total 18 published paper 6 scientific journal 12 international conference Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.021188486367464066, 0.012472002767026424, -0.03954780101776123, 0.0048455409705638885, -0.020418690517544746, 0.026509681716561317, 0.04262324795126915, 0.07307633012533188, -0.034143172204494476, 0.020297590643167496, 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671 | 671 | ['Anh Nguyen', 'Jason Yosinski', 'Jeff Clune'] | 1412.1897v4 | Deep neural networks (DNNs) have recently been achieving state-of-the-art
performance on a variety of pattern-recognition tasks, most notably visual
classification problems. Given that DNNs are now able to classify objects in
images with near-human-level performance, questions naturally arise as to what
differences remain between computer and human vision. A recent study revealed
that changing an image (e.g. of a lion) in a way imperceptible to humans can
cause a DNN to label the image as something else entirely (e.g. mislabeling a
lion a library). Here we show a related result: it is easy to produce images
that are completely unrecognizable to humans, but that state-of-the-art DNNs
believe to be recognizable objects with 99.99% confidence (e.g. labeling with
certainty that white noise static is a lion). Specifically, we take
convolutional neural networks trained to perform well on either the ImageNet or
MNIST datasets and then find images with evolutionary algorithms or gradient
ascent that DNNs label with high confidence as belonging to each dataset class.
It is possible to produce images totally unrecognizable to human eyes that DNNs
believe with near certainty are familiar objects, which we call "fooling
images" (more generally, fooling examples). Our results shed light on
interesting differences between human vision and current DNNs, and raise
questions about the generality of DNN computer vision. | Deep Neural Networks are Easily Fooled: High Confidence Predictions for
Unrecognizable Images | 2,014 | http://arxiv.org/pdf/1412.1897v4 | Title Deep Neural Networks Easily Fooled High Confidence Predictions Unrecognizable Images Summary Deep neural network DNNs recently achieving stateoftheart performance variety patternrecognition task notably visual classification problem Given DNNs able classify object image nearhumanlevel performance question naturally arise difference remain computer human vision recent study revealed changing image eg lion way imperceptible human cause DNN label image something else entirely eg mislabeling lion library show related result easy produce image completely unrecognizable human stateoftheart DNNs believe recognizable object 9999 confidence eg labeling certainty white noise static lion Specifically take convolutional neural network trained perform well either ImageNet MNIST datasets find image evolutionary algorithm gradient ascent DNNs label high confidence belonging dataset class possible produce image totally unrecognizable human eye DNNs believe near certainty familiar object call fooling image generally fooling example result shed light interesting difference human vision current DNNs raise question generality DNN computer vision Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.010116065852344036, 0.09094808995723724, -0.03562874346971512, 0.05571739003062248, -0.002176515059545636, 0.0061898017302155495, 0.055231500416994095, -0.015633421018719673, -0.032679539173841476, -0.002561143832281232, -0.01845097541809082, 0.036392152309417725, 0.026111774146556854, 0.05467673018574715, 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672 | 672 | ['Xinyu Wu', 'Vishal Saxena', 'Kehan Zhu'] | 1506.01072v2 | A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing. | Homogeneous Spiking Neuromorphic System for Real-World Pattern
Recognition | 2,015 | http://arxiv.org/pdf/1506.01072v2 | Title Homogeneous Spiking Neuromorphic System RealWorld Pattern Recognition Summary neuromorphic chip combine CMOS analog spiking neuron memristive synapsis offer promising solution braininspired computing provide massive neural network parallelism density Previous hybrid analog CMOSmemristor approach required extensive CMOS circuitry training thus eliminated density advantage gained adoption memristor synapsis used different waveform pre postsynaptic spike added undesirable circuit overhead describe hardware architecture feature large number memristor synapsis learn realworld pattern present versatile CMOS neuron combine integrateandfire behavior drive passive memristors implement competitive learning compact circuit module enables insitu plasticity memristor synapsis demonstrate handwrittendigits recognition using proposed architecture using transistorlevel circuit simulation described neuromorphic architecture homogeneous realizes fundamental building block largescale energyefficient braininspired silicon chip could lead nextgeneration cognitive computing Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.053706616163253784, -0.03694482147693634, -0.052267879247665405, 0.05803336948156357, 0.012484094128012657, 0.00016219884855672717, 0.010011780075728893, -0.00529831787571311, 0.0475807823240757, -0.001519152894616127, -0.042839165776968, 0.001372783095575869, -0.004398516844958067, 0.05477315932512283, 0.03150581941008568, 0.01080513559281826, 0.09416055679321289, 0.02448844164609909, 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673 | 673 | ['Ven Jyn Kok', 'Mei Kuan Lim', 'Chee Seng Chan'] | 1511.06586v1 | Although the traits emerged in a mass gathering are often non-deliberative,
the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold
increase of carnage in crowd since the past two decades has spurred significant
advances in the field of computer vision, towards effective and proactive crowd
surveillance. Computer vision stud- ies related to crowd are observed to
resonate with the understanding of the emergent behavior in physics (complex
systems) and biology (animal swarm). These studies, which are inspired by
biology and physics, share surprisingly common insights, and interesting
contradictions. However, this aspect of discussion has not been fully explored.
Therefore, this survey provides the readers with a review of the
state-of-the-art methods in crowd behavior analysis from the physics and
biologically inspired perspectives. We provide insights and comprehensive
discussions for a broader understanding of the underlying prospect of blending
physics and biology studies in computer vision. | Crowd Behavior Analysis: A Review where Physics meets Biology | 2,015 | http://arxiv.org/pdf/1511.06586v1 | Title Crowd Behavior Analysis Review Physics meet Biology Summary Although trait emerged mass gathering often nondeliberative act mass impulse may lead irre vocable crowd disaster twofold increase carnage crowd since past two decade spurred significant advance field computer vision towards effective proactive crowd surveillance Computer vision stud y related crowd observed resonate understanding emergent behavior physic complex system biology animal swarm study inspired biology physic share surprisingly common insight interesting contradiction However aspect discussion fully explored Therefore survey provides reader review stateoftheart method crowd behavior analysis physic biologically inspired perspective provide insight comprehensive discussion broader understanding underlying prospect blending physic biology study computer vision Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.002358394907787442, 0.015343783423304558, -0.02958426997065544, -0.014622044749557972, -0.010780510492622852, -0.021123582497239113, -0.0009222599328495562, 0.007528827525675297, -0.02057489939033985, 0.004340232815593481, 0.039204876869916916, 0.007756952196359634, -0.018435323610901833, 0.006776188500225544, 0.06967302411794662, -0.04676717519760132, -0.014992190524935722, -0.014454408548772335, -0.02882702462375164, 0.023137828335165977, 0.03667465224862099, -0.021968502551317215, 0.03130348399281502, 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674 | 674 | ['Vlado Menkovski', 'Zharko Aleksovski', 'Axel Saalbach', 'Hannes Nickisch'] | 1512.05986v1 | Convolutional neural networks demonstrated outstanding empirical results in
computer vision and speech recognition tasks where labeled training data is
abundant. In medical imaging, there is a huge variety of possible imaging
modalities and contrasts, where annotated data is usually very scarce. We
present two approaches to deal with this challenge. A network pretrained in a
different domain with abundant data is used as a feature extractor, while a
subsequent classifier is trained on a small target dataset; and a deep
architecture trained with heavy augmentation and equipped with sophisticated
regularization methods. We test the approaches on a corpus of X-ray images to
design an anatomy detection system. | Can Pretrained Neural Networks Detect Anatomy? | 2,015 | http://arxiv.org/pdf/1512.05986v1 | Title Pretrained Neural Networks Detect Anatomy Summary Convolutional neural network demonstrated outstanding empirical result computer vision speech recognition task labeled training data abundant medical imaging huge variety possible imaging modality contrast annotated data usually scarce present two approach deal challenge network pretrained different domain abundant data used feature extractor subsequent classifier trained small target dataset deep architecture trained heavy augmentation equipped sophisticated regularization method test approach corpus Xray image design anatomy detection system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.014294211752712727, 0.06194915249943733, -0.008607087656855583, 0.014003215357661247, 0.02963629737496376, 0.034846555441617966, 0.02385333739221096, 0.03210815414786339, 0.0029922490939497948, 0.022602366283535957, -0.048182617872953415, -0.0062265945598483086, 0.025089100003242493, 0.026192206889390945, 0.01063295267522335, -0.039213646203279495, -0.025880232453346252, 0.0574624203145504, -0.020232675597071648, -0.024425409734249115, 0.015635136514902115, -0.026697784662246704, -0.02849324606359005, 0.049248818308115005, -0.020592395216226578, 0.07767082750797272, 0.014530516229569912, -0.019640684127807617, 0.004279319662600756, -0.017848826944828033, -0.00040528448880650103, 0.026914680376648903, 0.0014189821667969227, -0.00782410055398941, 2.400156290605082e-06, -0.031882744282484055, 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675 | 675 | ['L. M. Rasdi Rere', 'Mohamad Ivan Fanany', 'Aniati Murni Arymurthy'] | 1610.01925v1 | A typical modern optimization technique is usually either heuristic or
metaheuristic. This technique has managed to solve some optimization problems
in the research area of science, engineering, and industry. However,
implementation strategy of metaheuristic for accuracy improvement on
convolution neural networks (CNN), a famous deep learning method, is still
rarely investigated. Deep learning relates to a type of machine learning
technique, where its aim is to move closer to the goal of artificial
intelligence of creating a machine that could successfully perform any
intellectual tasks that can be carried out by a human. In this paper, we
propose the implementation strategy of three popular metaheuristic approaches,
that is, simulated annealing, differential evolution, and harmony search, to
optimize CNN. The performances of these metaheuristic methods in optimizing CNN
on classifying MNIST and CIFAR dataset were evaluated and compared.
Furthermore, the proposed methods are also compared with the original CNN.
Although the proposed methods show an increase in the computation time, their
accuracy has also been improved (up to 7.14 percent). | Metaheuristic Algorithms for Convolution Neural Network | 2,016 | http://arxiv.org/pdf/1610.01925v1 | Title Metaheuristic Algorithms Convolution Neural Network Summary typical modern optimization technique usually either heuristic metaheuristic technique managed solve optimization problem research area science engineering industry However implementation strategy metaheuristic accuracy improvement convolution neural network CNN famous deep learning method still rarely investigated Deep learning relates type machine learning technique aim move closer goal artificial intelligence creating machine could successfully perform intellectual task carried human paper propose implementation strategy three popular metaheuristic approach simulated annealing differential evolution harmony search optimize CNN performance metaheuristic method optimizing CNN classifying MNIST CIFAR dataset evaluated compared Furthermore proposed method also compared original CNN Although proposed method show increase computation time accuracy also improved 714 percent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02029944397509098, 0.006519587244838476, -0.013802915811538696, 0.011240843683481216, -0.024036340415477753, -0.01870868168771267, 0.038991380482912064, -0.0033244576770812273, -0.07844418287277222, -0.0439758226275444, 0.003446210641413927, 0.030755717307329178, 0.011416098102927208, 0.026213426142930984, 0.0012278028298169374, -0.01054716482758522, 0.005024521145969629, 0.03585605323314667, -0.03613224998116493, 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676 | 676 | ['Jin-Hwa Kim', 'Kyoung-Woon On', 'Woosang Lim', 'Jeonghee Kim', 'Jung-Woo Ha', 'Byoung-Tak Zhang'] | 1610.04325v4 | Bilinear models provide rich representations compared with linear models.
They have been applied in various visual tasks, such as object recognition,
segmentation, and visual question-answering, to get state-of-the-art
performances taking advantage of the expanded representations. However,
bilinear representations tend to be high-dimensional, limiting the
applicability to computationally complex tasks. We propose low-rank bilinear
pooling using Hadamard product for an efficient attention mechanism of
multimodal learning. We show that our model outperforms compact bilinear
pooling in visual question-answering tasks with the state-of-the-art results on
the VQA dataset, having a better parsimonious property. | Hadamard Product for Low-rank Bilinear Pooling | 2,016 | http://arxiv.org/pdf/1610.04325v4 | Title Hadamard Product Lowrank Bilinear Pooling Summary Bilinear model provide rich representation compared linear model applied various visual task object recognition segmentation visual questionanswering get stateoftheart performance taking advantage expanded representation However bilinear representation tend highdimensional limiting applicability computationally complex task propose lowrank bilinear pooling using Hadamard product efficient attention mechanism multimodal learning show model outperforms compact bilinear pooling visual questionanswering task stateoftheart result VQA dataset better parsimonious property Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0004682496073655784, 0.014315168373286724, -0.02317340485751629, 0.02531825192272663, -0.029307929798960686, 0.017951473593711853, 0.04475909471511841, 0.023417161777615547, 0.02642686665058136, -0.04970598593354225, 0.0156943928450346, -0.0571695975959301, -0.01680913008749485, 0.017787916585803032, 0.0564383789896965, -0.09668026119470596, 0.014020562171936035, 0.020539548248052597, 0.008755335584282875, -0.04364711418747902, 0.032997675240039825, -0.020918628200888634, -0.02396223694086075, 0.02988833747804165, -0.0044812047854065895, -0.00732383644208312, -0.039443835616111755, -0.026128800585865974, 0.0031592596787959337, -0.013458057306706905, -0.03771952539682388, 0.0479285791516304, 0.03752826154232025, -0.03488185256719589, 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677 | 677 | ['Aojun Zhou', 'Anbang Yao', 'Yiwen Guo', 'Lin Xu', 'Yurong Chen'] | 1702.03044v2 | This paper presents incremental network quantization (INQ), a novel method,
targeting to efficiently convert any pre-trained full-precision convolutional
neural network (CNN) model into a low-precision version whose weights are
constrained to be either powers of two or zero. Unlike existing methods which
are struggled in noticeable accuracy loss, our INQ has the potential to resolve
this issue, as benefiting from two innovations. On one hand, we introduce three
interdependent operations, namely weight partition, group-wise quantization and
re-training. A well-proven measure is employed to divide the weights in each
layer of a pre-trained CNN model into two disjoint groups. The weights in the
first group are responsible to form a low-precision base, thus they are
quantized by a variable-length encoding method. The weights in the other group
are responsible to compensate for the accuracy loss from the quantization, thus
they are the ones to be re-trained. On the other hand, these three operations
are repeated on the latest re-trained group in an iterative manner until all
the weights are converted into low-precision ones, acting as an incremental
network quantization and accuracy enhancement procedure. Extensive experiments
on the ImageNet classification task using almost all known deep CNN
architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the
efficacy of the proposed method. Specifically, at 5-bit quantization, our
models have improved accuracy than the 32-bit floating-point references. Taking
ResNet-18 as an example, we further show that our quantized models with 4-bit,
3-bit and 2-bit ternary weights have improved or very similar accuracy against
its 32-bit floating-point baseline. Besides, impressive results with the
combination of network pruning and INQ are also reported. The code is available
at https://github.com/Zhouaojun/Incremental-Network-Quantization. | Incremental Network Quantization: Towards Lossless CNNs with
Low-Precision Weights | 2,017 | http://arxiv.org/pdf/1702.03044v2 | Title Incremental Network Quantization Towards Lossless CNNs LowPrecision Weights Summary paper present incremental network quantization INQ novel method targeting efficiently convert pretrained fullprecision convolutional neural network CNN model lowprecision version whose weight constrained either power two zero Unlike existing method struggled noticeable accuracy loss INQ potential resolve issue benefiting two innovation one hand introduce three interdependent operation namely weight partition groupwise quantization retraining wellproven measure employed divide weight layer pretrained CNN model two disjoint group weight first group responsible form lowprecision base thus quantized variablelength encoding method weight group responsible compensate accuracy loss quantization thus one retrained hand three operation repeated latest retrained group iterative manner weight converted lowprecision one acting incremental network quantization accuracy enhancement procedure Extensive experiment ImageNet classification task using almost known deep CNN architecture including AlexNet VGG16 GoogleNet ResNets well testify efficacy proposed method Specifically 5bit quantization model improved accuracy 32bit floatingpoint reference Taking ResNet18 example show quantized model 4bit 3bit 2bit ternary weight improved similar accuracy 32bit floatingpoint baseline Besides impressive result combination network pruning INQ also reported code available httpsgithubcomZhouaojunIncrementalNetworkQuantization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed 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678 | 678 | ['Dhanesh Ramachandram', 'Terrance DeVries'] | 1703.03372v3 | We present a method for skin lesion segmentation for the ISIC 2017 Skin
Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional
Network architecture which is trained end to end, from scratch, on a limited
dataset. Our semantic segmentation architecture utilizes several recent
innovations in particularly in the combined use of (i) use of atrous
convolutions to increase the effective field of view of the network's receptive
field without increasing the number of parameters, (ii) the use of
network-in-network $1\times1$ convolution layers to add capacity to the network
and (iii) state-of-art super-resolution upsampling of predictions using
subpixel CNN layers. We reported a mean IOU score of 0.642 on the validation
set provided by the organisers. | LesionSeg: Semantic segmentation of skin lesions using Deep
Convolutional Neural Network | 2,017 | http://arxiv.org/pdf/1703.03372v3 | Title LesionSeg Semantic segmentation skin lesion using Deep Convolutional Neural Network Summary present method skin lesion segmentation ISIC 2017 Skin Lesion Segmentation Challenge approach based Fully Convolutional Network architecture trained end end scratch limited dataset semantic segmentation architecture utilizes several recent innovation particularly combined use use atrous convolution increase effective field view network receptive field without increasing number parameter ii use networkinnetwork 1times1 convolution layer add capacity network iii stateofart superresolution upsampling prediction using subpixel CNN layer reported mean IOU score 0642 validation set provided organiser Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03243638575077057, -0.0013901983620598912, 0.012587620876729488, 0.012158333323895931, 0.03590473160147667, 0.008580729365348816, 0.02405596151947975, 0.016792617738246918, -0.015188582241535187, 0.024354035034775734, 0.021119659766554832, 0.024920549243688583, -0.012242786586284637, 0.035538893193006516, -0.005660500843077898, -0.04895271733403206, -0.01784798502922058, 0.05402134731411934, 0.009135732427239418, -0.021510815247893333, 0.03556530922651291, -0.021952729672193527, 0.017599578946828842, 0.07858692109584808, -0.09312640875577927, 0.011038034223020077, 0.04546112194657326, 0.010660200379788876, -0.016303062438964844, -0.07358961552381516, 0.02589031495153904, -0.04783903807401657, 0.036786045879125595, 0.014146128669381142, 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679 | 679 | ['Priyadarshini Panda', 'Gopalakrishnan Srinivasan', 'Kaushik Roy'] | 1703.03854v2 | Brain-inspired learning models attempt to mimic the cortical architecture and
computations performed in the neurons and synapses constituting the human brain
to achieve its efficiency in cognitive tasks. In this work, we present
convolutional spike timing dependent plasticity based feature learning with
biologically plausible leaky-integrate-and-fire neurons in Spiking Neural
Networks (SNNs). We use shared weight kernels that are trained to encode
representative features underlying the input patterns thereby improving the
sparsity as well as the robustness of the learning model. We demonstrate that
the proposed unsupervised learning methodology learns several visual categories
for object recognition with fewer number of examples and outperforms
traditional fully-connected SNN architectures while yielding competitive
accuracy. Additionally, we observe that the learning model performs out-of-set
generalization further making the proposed biologically plausible framework a
viable and efficient architecture for future neuromorphic applications. | Convolutional Spike Timing Dependent Plasticity based Feature Learning
in Spiking Neural Networks | 2,017 | http://arxiv.org/pdf/1703.03854v2 | Title Convolutional Spike Timing Dependent Plasticity based Feature Learning Spiking Neural Networks Summary Braininspired learning model attempt mimic cortical architecture computation performed neuron synapsis constituting human brain achieve efficiency cognitive task work present convolutional spike timing dependent plasticity based feature learning biologically plausible leakyintegrateandfire neuron Spiking Neural Networks SNNs use shared weight kernel trained encode representative feature underlying input pattern thereby improving sparsity well robustness learning model demonstrate proposed unsupervised learning methodology learns several visual category object recognition fewer number example outperforms traditional fullyconnected SNN architecture yielding competitive accuracy Additionally observe learning model performs outofset generalization making proposed biologically plausible framework viable efficient architecture future neuromorphic application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0192340686917305, 0.00753026083111763, -0.027655042707920074, 0.07184738665819168, 0.02239409275352955, -0.022748205810785294, 0.03357100859284401, -0.01638604700565338, 0.025535887107253075, 0.027913639321923256, -0.05634466931223869, 0.04285196214914322, 0.0009294417104683816, 0.09864293038845062, 0.028742263093590736, 0.005380721762776375, 0.08856036514043808, 0.038651157170534134, -0.013179244473576546, -0.049469877034425735, 0.03801804035902023, -0.055948976427316666, 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680 | 680 | ['Shumeet Baluja', 'Ian Fischer'] | 1703.09387v1 | Multiple different approaches of generating adversarial examples have been
proposed to attack deep neural networks. These approaches involve either
directly computing gradients with respect to the image pixels, or directly
solving an optimization on the image pixels. In this work, we present a
fundamentally new method for generating adversarial examples that is fast to
execute and provides exceptional diversity of output. We efficiently train
feed-forward neural networks in a self-supervised manner to generate
adversarial examples against a target network or set of networks. We call such
a network an Adversarial Transformation Network (ATN). ATNs are trained to
generate adversarial examples that minimally modify the classifier's outputs
given the original input, while constraining the new classification to match an
adversarial target class. We present methods to train ATNs and analyze their
effectiveness targeting a variety of MNIST classifiers as well as the latest
state-of-the-art ImageNet classifier Inception ResNet v2. | Adversarial Transformation Networks: Learning to Generate Adversarial
Examples | 2,017 | http://arxiv.org/pdf/1703.09387v1 | Title Adversarial Transformation Networks Learning Generate Adversarial Examples Summary Multiple different approach generating adversarial example proposed attack deep neural network approach involve either directly computing gradient respect image pixel directly solving optimization image pixel work present fundamentally new method generating adversarial example fast execute provides exceptional diversity output efficiently train feedforward neural network selfsupervised manner generate adversarial example target network set network call network Adversarial Transformation Network ATN ATNs trained generate adversarial example minimally modify classifier output given original input constraining new classification match adversarial target class present method train ATNs analyze effectiveness targeting variety MNIST classifier well latest stateoftheart ImageNet classifier Inception ResNet v2 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02422841638326645, 0.03188668191432953, -0.02534564398229122, 0.036693260073661804, -0.010438768193125725, -0.04462973400950432, 0.057960670441389084, -0.024842381477355957, -0.05617695674300194, -0.027730831876397133, -0.02204558625817299, 0.06027965992689133, 0.011222568340599537, 0.022123096510767937, 0.05982523038983345, -0.042375411838293076, 0.00494662718847394, 0.04537256434559822, -0.09608642011880875, -0.026640651747584343, -0.01748872548341751, -0.02719542756676674, 0.015959663316607475, 0.01929156482219696, -0.024003081023693085, 0.02274348773062229, 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681 | 681 | ['Devinder Kumar', 'Graham W Taylor', 'Alexander Wong'] | 1709.01574v1 | Deep learning has been shown to outperform traditional machine learning
algorithms across a wide range of problem domains. However, current deep
learning algorithms have been criticized as uninterpretable "black-boxes" which
cannot explain their decision making processes. This is a major shortcoming
that prevents the widespread application of deep learning to domains with
regulatory processes such as finance. As such, industries such as finance have
to rely on traditional models like decision trees that are much more
interpretable but less effective than deep learning for complex problems. In
this paper, we propose CLEAR-Trade, a novel financial AI visualization
framework for deep learning-driven stock market prediction that mitigates the
interpretability issue of deep learning methods. In particular, CLEAR-Trade
provides a effective way to visualize and explain decisions made by deep stock
market prediction models. We show the efficacy of CLEAR-Trade in enhancing the
interpretability of stock market prediction by conducting experiments based on
S&P 500 stock index prediction. The results demonstrate that CLEAR-Trade can
provide significant insight into the decision-making process of deep
learning-driven financial models, particularly for regulatory processes, thus
improving their potential uptake in the financial industry. | Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced
Attentive Response Approach for Explaining and Visualizing Deep
Learning-Driven Stock Market Prediction | 2,017 | http://arxiv.org/pdf/1709.01574v1 | Title Opening Black Box Financial AI CLEARTrade CLassEnhanced Attentive Response Approach Explaining Visualizing Deep LearningDriven Stock Market Prediction Summary Deep learning shown outperform traditional machine learning algorithm across wide range problem domain However current deep learning algorithm criticized uninterpretable blackboxes cannot explain decision making process major shortcoming prevents widespread application deep learning domain regulatory process finance industry finance rely traditional model like decision tree much interpretable le effective deep learning complex problem paper propose CLEARTrade novel financial AI visualization framework deep learningdriven stock market prediction mitigates interpretability issue deep learning method particular CLEARTrade provides effective way visualize explain decision made deep stock market prediction model show efficacy CLEARTrade enhancing interpretability stock market prediction conducting experiment based SP 500 stock index prediction result demonstrate CLEARTrade provide significant insight decisionmaking process deep learningdriven financial model particularly regulatory process thus improving potential uptake financial industry Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.040640443563461304, 0.016146590933203697, -0.05602003633975983, -0.03990969434380531, 0.028110221028327942, -0.006837813183665276, 0.04842669889330864, 0.03750529885292053, -0.010637651197612286, 0.01545463316142559, 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682 | 682 | ['Mohammad Javad Shafiee', 'Brendan Chywl', 'Francis Li', 'Alexander Wong'] | 1709.05943v1 | Object detection is considered one of the most challenging problems in this
field of computer vision, as it involves the combination of object
classification and object localization within a scene. Recently, deep neural
networks (DNNs) have been demonstrated to achieve superior object detection
performance compared to other approaches, with YOLOv2 (an improved You Only
Look Once model) being one of the state-of-the-art in DNN-based object
detection methods in terms of both speed and accuracy. Although YOLOv2 can
achieve real-time performance on a powerful GPU, it still remains very
challenging for leveraging this approach for real-time object detection in
video on embedded computing devices with limited computational power and
limited memory. In this paper, we propose a new framework called Fast YOLO, a
fast You Only Look Once framework which accelerates YOLOv2 to be able to
perform object detection in video on embedded devices in a real-time manner.
First, we leverage the evolutionary deep intelligence framework to evolve the
YOLOv2 network architecture and produce an optimized architecture (referred to
as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To
further reduce power consumption on embedded devices while maintaining
performance, a motion-adaptive inference method is introduced into the proposed
Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2
based on temporal motion characteristics. Experimental results show that the
proposed Fast YOLO framework can reduce the number of deep inferences by an
average of 38.13%, and an average speedup of ~3.3X for objection detection in
video compared to the original YOLOv2, leading Fast YOLO to run an average of
~18FPS on a Nvidia Jetson TX1 embedded system. | Fast YOLO: A Fast You Only Look Once System for Real-time Embedded
Object Detection in Video | 2,017 | http://arxiv.org/pdf/1709.05943v1 | Title Fast YOLO Fast Look System Realtime Embedded Object Detection Video Summary Object detection considered one challenging problem field computer vision involves combination object classification object localization within scene Recently deep neural network DNNs demonstrated achieve superior object detection performance compared approach YOLOv2 improved Look model one stateoftheart DNNbased object detection method term speed accuracy Although YOLOv2 achieve realtime performance powerful GPU still remains challenging leveraging approach realtime object detection video embedded computing device limited computational power limited memory paper propose new framework called Fast YOLO fast Look framework accelerates YOLOv2 able perform object detection video embedded device realtime manner First leverage evolutionary deep intelligence framework evolve YOLOv2 network architecture produce optimized architecture referred OYOLOv2 28X fewer parameter 2 IOU drop reduce power consumption embedded device maintaining performance motionadaptive inference method introduced proposed Fast YOLO framework reduce frequency deep inference OYOLOv2 based temporal motion characteristic Experimental result show proposed Fast YOLO framework reduce number deep inference average 3813 average speedup 33X objection detection video compared original YOLOv2 leading Fast YOLO run average 18FPS Nvidia Jetson TX1 embedded system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.014544973149895668, 0.03064371645450592, 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683 | 683 | ['Xiaoliang Dai', 'Hongxu Yin', 'Niraj K. Jha'] | 1711.02017v2 | Neural networks (NNs) have begun to have a pervasive impact on various
applications of machine learning. However, the problem of finding an optimal NN
architecture for large applications has remained open for several decades.
Conventional approaches search for the optimal NN architecture through
extensive trial-and-error. Such a procedure is quite inefficient. In addition,
the generated NN architectures incur substantial redundancy. To address these
problems, we propose an NN synthesis tool (NeST) that automatically generates
very compact architectures for a given dataset. NeST starts with a seed NN
architecture. It iteratively tunes the architecture with gradient-based growth
and magnitude-based pruning of neurons and connections. Our experimental
results show that NeST yields accurate yet very compact NNs with a wide range
of seed architecture selection. For example, for the LeNet-300-100 (LeNet-5) NN
architecture derived from the MNIST dataset, we reduce network parameters by
34.1x (74.3x) and floating-point operations (FLOPs) by 35.8x (43.7x). For the
AlexNet NN architecture derived from the ImageNet dataset, we reduce network
parameters by 15.7x and FLOPs by 4.6x. All these results are the current
state-of-the-art for these architectures. | NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm | 2,017 | http://arxiv.org/pdf/1711.02017v2 | Title NeST Neural Network Synthesis Tool Based GrowandPrune Paradigm Summary Neural network NNs begun pervasive impact various application machine learning However problem finding optimal NN architecture large application remained open several decade Conventional approach search optimal NN architecture extensive trialanderror procedure quite inefficient addition generated NN architecture incur substantial redundancy address problem propose NN synthesis tool NeST automatically generates compact architecture given dataset NeST start seed NN architecture iteratively tune architecture gradientbased growth magnitudebased pruning neuron connection experimental result show NeST yield accurate yet compact NNs wide range seed architecture selection example LeNet300100 LeNet5 NN architecture derived MNIST dataset reduce network parameter 341x 743x floatingpoint operation FLOPs 358x 437x AlexNet NN architecture derived ImageNet dataset reduce network parameter 157x FLOPs 46x result current stateoftheart architecture Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04225454106926918, 0.045149169862270355, -0.055379170924425125, 0.027144653722643852, -0.03237073868513107, -0.04498456418514252, 0.04806896671652794, -0.011014611460268497, 0.014655706472694874, 0.025836342945694923, 0.0127420574426651, 0.005657525733113289, 0.005699627101421356, 0.08892931044101715, 0.020837491378188133, 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684 | 684 | ['Filipe Rolim Cordeiro', 'Wellington Pinheiro dos Santos', 'Abel Guilhermino da Silva Filho'] | 1712.07312v1 | Breast cancer is already one of the most common form of cancer worldwide.
Mammography image analysis is still the most effective diagnostic method to
promote the early detection of breast cancer. Accurately segmenting tumors in
digital mammography images is important to improve diagnosis capabilities of
health specialists and avoid misdiagnosis. In this work, we evaluate the
feasibility of applying GrowCut to segment regions of tumor and we propose two
GrowCut semi-supervised versions. All the analysis was performed by evaluating
the application of segmentation techniques to a set of images obtained from the
Mini-MIAS mammography image database. GrowCut segmentation was compared to
Region Growing, Active Contours, Random Walks and Graph Cut techniques.
Experiments showed that GrowCut, when compared to the other techniques, was
able to acquire better results for the metrics analyzed. Moreover, the proposed
semi-supervised versions of GrowCut was proved to have a clinically
satisfactory quality of segmentation. | Analysis of supervised and semi-supervised GrowCut applied to
segmentation of masses in mammography images | 2,017 | http://arxiv.org/pdf/1712.07312v1 | Title Analysis supervised semisupervised GrowCut applied segmentation mass mammography image Summary Breast cancer already one common form cancer worldwide Mammography image analysis still effective diagnostic method promote early detection breast cancer Accurately segmenting tumor digital mammography image important improve diagnosis capability health specialist avoid misdiagnosis work evaluate feasibility applying GrowCut segment region tumor propose two GrowCut semisupervised version analysis performed evaluating application segmentation technique set image obtained MiniMIAS mammography image database GrowCut segmentation compared Region Growing Active Contours Random Walks Graph Cut technique Experiments showed GrowCut compared technique able acquire better result metric analyzed Moreover proposed semisupervised version GrowCut proved clinically satisfactory quality segmentation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.016599994152784348, -0.0029411392752081156, -0.023990413174033165, 0.001886810059659183, -0.05486414209008217, -0.0001775944692781195, 0.03415572643280029, 0.037300389260053635, 0.02353079617023468, 0.031302161514759064, 0.07191748917102814, 0.01975228823721409, -0.01802855171263218, 0.07661595195531845, -0.02419896423816681, -0.04990557208657265, -0.07463978230953217, -0.002934993477538228, -0.03860204666852951, -0.024571917951107025, 0.001499780802987516, -0.04105520248413086, -0.005922118201851845, 0.006533666979521513, 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685 | 685 | ['Shumeet Baluja'] | 1801.05156v1 | We present extensive experiments training and testing hidden units in deep
networks that emit only a predefined, static, number of discretized values.
These units provide benefits in real-world deployment in systems in which
memory and/or computation may be limited. Additionally, they are particularly
well suited for use in large recurrent network models that require the
maintenance of large amounts of internal state in memory. Surprisingly, we find
that despite reducing the number of values that can be represented in the
output activations from $2^{32}-2^{64}$ to between 64 and 256, there is little
to no degradation in network performance across a variety of different
settings. We investigate simple classification and regression tasks, as well as
memorization and compression problems. We compare the results with more
standard activations, such as tanh and relu. Unlike previous discretization
studies which often concentrate only on binary units, we examine the effects of
varying the number of allowed activation levels. Compared to existing
approaches for discretization, the approach presented here is both conceptually
and programatically simple, has no stochastic component, and allows the
training, testing, and usage phases to be treated in exactly the same manner. | Empirical Explorations in Training Networks with Discrete Activations | 2,018 | http://arxiv.org/pdf/1801.05156v1 | Title Empirical Explorations Training Networks Discrete Activations Summary present extensive experiment training testing hidden unit deep network emit predefined static number discretized value unit provide benefit realworld deployment system memory andor computation may limited Additionally particularly well suited use large recurrent network model require maintenance large amount internal state memory Surprisingly find despite reducing number value represented output activation 232264 64 256 little degradation network performance across variety different setting investigate simple classification regression task well memorization compression problem compare result standard activation tanh relu Unlike previous discretization study often concentrate binary unit examine effect varying number allowed activation level Compared existing approach discretization approach presented conceptually programatically simple stochastic component allows training testing usage phase treated exactly manner Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.012845207937061787, 0.0042851511389017105, -0.014024388045072556, 0.0053544011898338795, 0.03046133928000927, -0.0171323474496603, 0.07774453610181808, -0.008793994784355164, -0.017757412046194077, 0.0006350188632495701, 0.010669970884919167, -0.01071455143392086, -0.007712224032729864, 0.038528744131326675, 0.021989526227116585, 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686 | 686 | ['Esteban Real', 'Alok Aggarwal', 'Yanping Huang', 'Quoc V Le'] | 1802.01548v3 | The effort devoted to hand-crafting image classifiers has motivated the use
of architecture search to discover them automatically. Reinforcement learning
and evolution have both shown promise for this purpose. This study employs a
regularized version of a popular asynchronous evolutionary algorithm. We
rigorously compare it to the non-regularized form and to a highly-successful
reinforcement learning baseline. Using the same hardware, compute effort and
neural network training code, we conduct repeated experiments side-by-side,
exploring different datasets, search spaces and scales. We show regularized
evolution consistently produces models with similar or higher accuracy, across
a variety of contexts without need for re-tuning parameters. In addition,
evolution exhibits considerably better performance than reinforcement learning
at early search stages, suggesting it may be the better choice when fewer
compute resources are available. This constitutes the first controlled
comparison of the two search algorithms in this context. Finally, we present
new architectures discovered with evolution that we nickname AmoebaNets. These
models achieve state-of-the-art results for CIFAR-10 (mean test error = 2.13%),
mobile-size ImageNet (top-1 accuracy = 75.1% with 5.1 M parameters) and
ImageNet (top-1 accuracy = 83.1%). This is the first time evolutionary
algorithms produce state-of-the-art image classifiers. | Regularized Evolution for Image Classifier Architecture Search | 2,018 | http://arxiv.org/pdf/1802.01548v3 | Title Regularized Evolution Image Classifier Architecture Search Summary effort devoted handcrafting image classifier motivated use architecture search discover automatically Reinforcement learning evolution shown promise purpose study employ regularized version popular asynchronous evolutionary algorithm rigorously compare nonregularized form highlysuccessful reinforcement learning baseline Using hardware compute effort neural network training code conduct repeated experiment sidebyside exploring different datasets search space scale show regularized evolution consistently produce model similar higher accuracy across variety context without need retuning parameter addition evolution exhibit considerably better performance reinforcement learning early search stage suggesting may better choice fewer compute resource available constitutes first controlled comparison two search algorithm context Finally present new architecture discovered evolution nickname AmoebaNets model achieve stateoftheart result CIFAR10 mean test error 213 mobilesize ImageNet top1 accuracy 751 51 parameter ImageNet top1 accuracy 831 first time evolutionary algorithm produce stateoftheart image classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.022280516102910042, 0.049255795776844025, -0.04031810164451599, 0.014892525039613247, -0.008276914246380329, -0.026720911264419556, 0.013798962347209454, 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687 | 687 | ['Alexander Wong', 'Mohammad Javad Shafiee', 'Francis Li', 'Brendan Chwyl'] | 1802.06488v1 | Object detection is a major challenge in computer vision, involving both
object classification and object localization within a scene. While deep neural
networks have been shown in recent years to yield very powerful techniques for
tackling the challenge of object detection, one of the biggest challenges with
enabling such object detection networks for widespread deployment on embedded
devices is high computational and memory requirements. Recently, there has been
an increasing focus in exploring small deep neural network architectures for
object detection that are more suitable for embedded devices, such as Tiny YOLO
and SqueezeDet. Inspired by the efficiency of the Fire microarchitecture
introduced in SqueezeNet and the object detection performance of the
single-shot detection macroarchitecture introduced in SSD, this paper
introduces Tiny SSD, a single-shot detection deep convolutional neural network
for real-time embedded object detection that is composed of a highly optimized,
non-uniform Fire sub-network stack and a non-uniform sub-network stack of
highly optimized SSD-based auxiliary convolutional feature layers designed
specifically to minimize model size while maintaining object detection
performance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller
than Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher
than Tiny YOLO). These experimental results show that very small deep neural
network architectures can be designed for real-time object detection that are
well-suited for embedded scenarios. | Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network
for Real-time Embedded Object Detection | 2,018 | http://arxiv.org/pdf/1802.06488v1 | Title Tiny SSD Tiny Singleshot Detection Deep Convolutional Neural Network Realtime Embedded Object Detection Summary Object detection major challenge computer vision involving object classification object localization within scene deep neural network shown recent year yield powerful technique tackling challenge object detection one biggest challenge enabling object detection network widespread deployment embedded device high computational memory requirement Recently increasing focus exploring small deep neural network architecture object detection suitable embedded device Tiny YOLO SqueezeDet Inspired efficiency Fire microarchitecture introduced SqueezeNet object detection performance singleshot detection macroarchitecture introduced SSD paper introduces Tiny SSD singleshot detection deep convolutional neural network realtime embedded object detection composed highly optimized nonuniform Fire subnetwork stack nonuniform subnetwork stack highly optimized SSDbased auxiliary convolutional feature layer designed specifically minimize model size maintaining object detection performance resulting Tiny SSD posse model size 23MB 26X smaller Tiny YOLO still achieving mAP 613 VOC 2007 42 higher Tiny YOLO experimental result show small deep neural network architecture designed realtime object detection wellsuited embedded scenario Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.00894768163561821, -0.015540339052677155, -0.010371421463787556, 0.09251029044389725, 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688 | 688 | ['Tobias Hinz', 'Stefan Wermter'] | 1803.02627v1 | Combining Generative Adversarial Networks (GANs) with encoders that learn to
encode data points has shown promising results in learning data representations
in an unsupervised way. We propose a framework that combines an encoder and a
generator to learn disentangled representations which encode meaningful
information about the data distribution without the need for any labels. While
current approaches focus mostly on the generative aspects of GANs, our
framework can be used to perform inference on both real and generated data
points. Experiments on several data sets show that the encoder learns
interpretable, disentangled representations which encode descriptive properties
and can be used to sample images that exhibit specific characteristics. | Inferencing Based on Unsupervised Learning of Disentangled
Representations | 2,018 | http://arxiv.org/pdf/1803.02627v1 | Title Inferencing Based Unsupervised Learning Disentangled Representations Summary Combining Generative Adversarial Networks GANs encoders learn encode data point shown promising result learning data representation unsupervised way propose framework combine encoder generator learn disentangled representation encode meaningful information data distribution without need label current approach focus mostly generative aspect GANs framework used perform inference real generated data point Experiments several data set show encoder learns interpretable disentangled representation encode descriptive property used sample image exhibit specific characteristic Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03528452292084694, 0.12660232186317444, -0.018537303432822227, 0.07999861240386963, 0.01150921918451786, 0.03174477815628052, -0.004988578148186207, 0.017566945403814316, -0.007421194110065699, 0.03771132230758667, -0.01803317666053772, 0.05032367631793022, -0.010365442372858524, 0.03543653339147568, 0.06172604486346245, -0.08216636627912521, -0.01403383444994688, 0.021129725500941277, -0.04058736562728882, -0.0323559008538723, 0.017009196802973747, -0.021326933056116104, 0.00406910665333271, 0.018920423462986946, -0.027589038014411926, 0.0002733043802436441, -0.0204454492777586, 0.011871473863720894, 0.034429412335157394, -0.02150152251124382, 0.0040621827356517315, 0.013605329208076, 0.0508461520075798, -0.00432587368413806, 2.3975612748472486e-06, -0.0013971893349662423, -0.010605962947010994, 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689 | 689 | ['Erik Berglund', 'Joaquin Sitte'] | 0705.0199v2 | The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network
algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a
learning rate and annealing schemes for learning rate and neighbourhood size.
We discuss the relative performance of the PLSOM and the SOM and demonstrate
some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally
we discuss some example applications of the PLSOM and present a proof of
ordering under certain limited conditions. | The Parameter-Less Self-Organizing Map algorithm | 2,007 | http://arxiv.org/pdf/0705.0199v2 | Title ParameterLess SelfOrganizing Map algorithm Summary ParameterLess SelfOrganizing Map PLSOM new neural network algorithm based SelfOrganizing Map SOM eliminates need learning rate annealing scheme learning rate neighbourhood size discus relative performance PLSOM SOM demonstrate task SOM fails PLSOM performs satisfactory Finally discus example application PLSOM present proof ordering certain limited condition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 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690 | 690 | ['Christian Napoli', 'Giuseppe Pappalardo', 'Emiliano Tramontana', 'Zbigniew Marszałek', 'Dawid Połap', 'Marcin Woźniak'] | 1412.6464v1 | In order to identify an object, human eyes firstly search the field of view
for points or areas which have particular properties. These properties are used
to recognise an image or an object. Then this process could be taken as a model
to develop computer algorithms for images identification. This paper proposes
the idea of applying the simplified firefly algorithm to search for key-areas
in 2D images. For a set of input test images the proposed version of firefly
algorithm has been examined. Research results are presented and discussed to
show the efficiency of this evolutionary computation method. | Simplified firefly algorithm for 2D image key-points search | 2,014 | http://arxiv.org/pdf/1412.6464v1 | Title Simplified firefly algorithm 2D image keypoints search Summary order identify object human eye firstly search field view point area particular property property used recognise image object process could taken model develop computer algorithm image identification paper proposes idea applying simplified firefly algorithm search keyareas 2D image set input test image proposed version firefly algorithm examined Research result presented discussed show efficiency evolutionary computation method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.00666049076244235, -0.007869566790759563, -0.003758190432563424, 0.035692159086465836, -0.020446600392460823, -0.0026361218187958, 3.1921383197186515e-05, 0.037386175245046616, 0.006876166444271803, -0.03355340287089348, 0.014403634704649448, 0.05032142996788025, 0.02732980065047741, -0.0015734510961920023, 0.003372750012204051, -0.04399307817220688, -0.04577961936593056, 0.014932246878743172, -0.009439398534595966, 0.00260493578389287, 0.05614076927304268, -0.05433954671025276, 0.03141295164823532, -0.018458139151334763, -0.04392598569393158, 0.012387044727802277, -0.0471278578042984, -0.026661928743124008, 0.09728733450174332, -0.01529128011316061, 0.004858111031353474, 0.012330455705523491, -0.034243084490299225, 0.05022239685058594, 2.117094481945969e-06, -0.04964103177189827, -0.03452523052692413, 0.014187508262693882, 0.09107588231563568, 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691 | 691 | ['Sue Han Lee', 'Chee Seng Chan', 'Paul Wilkin', 'Paolo Remagnino'] | 1506.08425v1 | This paper studies convolutional neural networks (CNN) to learn unsupervised
feature representations for 44 different plant species, collected at the Royal
Botanic Gardens, Kew, England. To gain intuition on the chosen features from
the CNN model (opposed to a 'black box' solution), a visualisation technique
based on the deconvolutional networks (DN) is utilized. It is found that
venations of different order have been chosen to uniquely represent each of the
plant species. Experimental results using these CNN features with different
classifiers show consistency and superiority compared to the state-of-the art
solutions which rely on hand-crafted features. | Deep-Plant: Plant Identification with convolutional neural networks | 2,015 | http://arxiv.org/pdf/1506.08425v1 | Title DeepPlant Plant Identification convolutional neural network Summary paper study convolutional neural network CNN learn unsupervised feature representation 44 different plant specie collected Royal Botanic Gardens Kew England gain intuition chosen feature CNN model opposed black box solution visualisation technique based deconvolutional network DN utilized found venation different order chosen uniquely represent plant specie Experimental result using CNN feature different classifier show consistency superiority compared stateofthe art solution rely handcrafted feature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.006776423193514347, 0.017540596425533295, -0.013493082486093044, 0.09937453269958496, -0.015553660690784454, -0.001622108044102788, 0.02896924503147602, 0.015895109623670578, -0.029217569157481194, 0.021072691306471825, 0.00834373477846384, 0.033706702291965485, -0.00636943057179451, 0.03164862096309662, -0.004153152462095022, -0.01186476182192564, -0.03399576246738434, 0.027491280809044838, 0.02510054223239422, -0.0332333967089653, -0.006542478688061237, 0.004884540569037199, 0.005666360259056091, 0.0405256524682045, -0.09127219766378403, -0.010085362941026688, -0.013601613231003284, 0.04156212881207466, 0.028146130964159966, -0.0036231130361557007, 0.02483411878347397, -0.0012811803026124835, -0.018415285274386406, 0.0451512411236763, 1.8308194285054924e-06, -0.018171237781643867, 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692 | 692 | ['Joshua C. Peterson', 'Joshua T. Abbott', 'Thomas L. Griffiths'] | 1608.02164v1 | Deep neural networks have become increasingly successful at solving classic
perception problems such as object recognition, semantic segmentation, and
scene understanding, often reaching or surpassing human-level accuracy. This
success is due in part to the ability of DNNs to learn useful representations
of high-dimensional inputs, a problem that humans must also solve. We examine
the relationship between the representations learned by these networks and
human psychological representations recovered from similarity judgments. We
find that deep features learned in service of object classification account for
a significant amount of the variance in human similarity judgments for a set of
animal images. However, these features do not capture some qualitative
distinctions that are a key part of human representations. To remedy this, we
develop a method for adapting deep features to align with human similarity
judgments, resulting in image representations that can potentially be used to
extend the scope of psychological experiments. | Adapting Deep Network Features to Capture Psychological Representations | 2,016 | http://arxiv.org/pdf/1608.02164v1 | Title Adapting Deep Network Features Capture Psychological Representations Summary Deep neural network become increasingly successful solving classic perception problem object recognition semantic segmentation scene understanding often reaching surpassing humanlevel accuracy success due part ability DNNs learn useful representation highdimensional input problem human must also solve examine relationship representation learned network human psychological representation recovered similarity judgment find deep feature learned service object classification account significant amount variance human similarity judgment set animal image However feature capture qualitative distinction key part human representation remedy develop method adapting deep feature align human similarity judgment resulting image representation potentially used extend scope psychological experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.013091064058244228, 0.07363045960664749, -0.03308088704943657, 0.0372568815946579, -0.01791868545114994, 0.02727382630109787, 0.03783806413412094, 0.00030163361225277185, -0.021288791671395302, -0.01098821684718132, -0.06715395301580429, -0.002083749743178487, 0.015625564381480217, 0.05016550421714783, 0.012282148003578186, -0.09944529831409454, -0.03597528114914894, 0.08667001873254776, -0.012709474191069603, -0.0013389369705691934, -0.013358726166188717, 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693 | 693 | ['Esteban Real', 'Sherry Moore', 'Andrew Selle', 'Saurabh Saxena', 'Yutaka Leon Suematsu', 'Jie Tan', 'Quoc Le', 'Alex Kurakin'] | 1703.01041v2 | Neural networks have proven effective at solving difficult problems but
designing their architectures can be challenging, even for image classification
problems alone. Our goal is to minimize human participation, so we employ
evolutionary algorithms to discover such networks automatically. Despite
significant computational requirements, we show that it is now possible to
evolve models with accuracies within the range of those published in the last
year. Specifically, we employ simple evolutionary techniques at unprecedented
scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting
from trivial initial conditions and reaching accuracies of 94.6% (95.6% for
ensemble) and 77.0%, respectively. To do this, we use novel and intuitive
mutation operators that navigate large search spaces; we stress that no human
participation is required once evolution starts and that the output is a
fully-trained model. Throughout this work, we place special emphasis on the
repeatability of results, the variability in the outcomes and the computational
requirements. | Large-Scale Evolution of Image Classifiers | 2,017 | http://arxiv.org/pdf/1703.01041v2 | Title LargeScale Evolution Image Classifiers Summary Neural network proven effective solving difficult problem designing architecture challenging even image classification problem alone goal minimize human participation employ evolutionary algorithm discover network automatically Despite significant computational requirement show possible evolve model accuracy within range published last year Specifically employ simple evolutionary technique unprecedented scale discover model CIFAR10 CIFAR100 datasets starting trivial initial condition reaching accuracy 946 956 ensemble 770 respectively use novel intuitive mutation operator navigate large search space stress human participation required evolution start output fullytrained model Throughout work place special emphasis repeatability result variability outcome computational requirement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.011365228332579136, 0.07445085048675537, -0.04380756616592407, 0.036304619163274765, 0.010352357290685177, 0.0017203945899382234, 0.03758206218481064, 0.03093908540904522, -0.012030882760882378, 0.019904321059584618, 0.005386252887547016, 0.03029400296509266, -0.007501552812755108, 0.0726308524608612, 0.022112620994448662, -0.017253298312425613, -0.014833632856607437, 0.028817193582654, -0.06813610345125198, -0.02956106886267662, 0.02470865286886692, -0.03570472449064255, 0.04504650458693504, 0.023100724443793297, -0.018178163096308708, 0.03337155282497406, -0.04116322100162506, 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694 | 694 | ['Chunpeng Wu', 'Wei Wen', 'Tariq Afzal', 'Yongmei Zhang', 'Yiran Chen', 'Hai Li'] | 1703.04071v4 | Recently, DNN model compression based on network architecture design, e.g.,
SqueezeNet, attracted a lot attention. No accuracy drop on image classification
is observed on these extremely compact networks, compared to well-known models.
An emerging question, however, is whether these model compression techniques
hurt DNN's learning ability other than classifying images on a single dataset.
Our preliminary experiment shows that these compression methods could degrade
domain adaptation (DA) ability, though the classification performance is
preserved. Therefore, we propose a new compact network architecture and
unsupervised DA method in this paper. The DNN is built on a new basic module
Conv-M which provides more diverse feature extractors without significantly
increasing parameters. The unified framework of our DA method will
simultaneously learn invariance across domains, reduce divergence of feature
representations, and adapt label prediction. Our DNN has 4.1M parameters, which
is only 6.7% of AlexNet or 59% of GoogLeNet. Experiments show that our DNN
obtains GoogLeNet-level accuracy both on classification and DA, and our DA
method slightly outperforms previous competitive ones. Put all together, our DA
strategy based on our DNN achieves state-of-the-art on sixteen of total
eighteen DA tasks on popular Office-31 and Office-Caltech datasets. | A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification
and Domain Adaptation | 2,017 | http://arxiv.org/pdf/1703.04071v4 | Title Compact DNN Approaching GoogLeNetLevel Accuracy Classification Domain Adaptation Summary Recently DNN model compression based network architecture design eg SqueezeNet attracted lot attention accuracy drop image classification observed extremely compact network compared wellknown model emerging question however whether model compression technique hurt DNNs learning ability classifying image single dataset preliminary experiment show compression method could degrade domain adaptation DA ability though classification performance preserved Therefore propose new compact network architecture unsupervised DA method paper DNN built new basic module ConvM provides diverse feature extractor without significantly increasing parameter unified framework DA method simultaneously learn invariance across domain reduce divergence feature representation adapt label prediction DNN 41M parameter 67 AlexNet 59 GoogLeNet Experiments show DNN obtains GoogLeNetlevel accuracy classification DA DA method slightly outperforms previous competitive one Put together DA strategy based DNN achieves stateoftheart sixteen total eighteen DA task popular Office31 OfficeCaltech datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.012206400744616985, 0.05085603520274162, -0.028635334223508835, 0.0407564640045166, -0.010684553533792496, 0.003392936196178198, 0.05592728778719902, 0.02588380128145218, -0.03154008463025093, 0.02351333387196064, -0.022899260744452477, 0.01830415055155754, 0.018779441714286804, 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695 | 695 | ['Mandar Haldekar', 'Ashwinkumar Ganesan', 'Tim Oates'] | 1706.04215v1 | Traditional approaches to building a large scale knowledge graph have usually
relied on extracting information (entities, their properties, and relations
between them) from unstructured text (e.g. Dbpedia). Recent advances in
Convolutional Neural Networks (CNN) allow us to shift our focus to learning
entities and relations from images, as they build robust models that require
little or no pre-processing of the images. In this paper, we present an
approach to identify and extract spatial relations (e.g., The girl is standing
behind the table) from images using CNNs. Our research addresses two specific
challenges: providing insight into how spatial relations are learned by the
network and which parts of the image are used to predict these relations. We
use the pre-trained network VGGNet to extract features from an image and train
a Multi-layer Perceptron (MLP) on a set of synthetic images and the sun09
dataset to extract spatial relations. The MLP predicts spatial relations
without a bounding box around the objects or the space in the image depicting
the relation. To understand how the spatial relations are represented in the
network, a heatmap is overlayed on the image to show the regions that are
deemed important by the network. Also, we analyze the MLP to show the
relationship between the activation of consistent groups of nodes and the
prediction of a spatial relation. We show how the loss of these groups affects
the networks ability to identify relations. | Identifying Spatial Relations in Images using Convolutional Neural
Networks | 2,017 | http://arxiv.org/pdf/1706.04215v1 | Title Identifying Spatial Relations Images using Convolutional Neural Networks Summary Traditional approach building large scale knowledge graph usually relied extracting information entity property relation unstructured text eg Dbpedia Recent advance Convolutional Neural Networks CNN allow u shift focus learning entity relation image build robust model require little preprocessing image paper present approach identify extract spatial relation eg girl standing behind table image using CNNs research address two specific challenge providing insight spatial relation learned network part image used predict relation use pretrained network VGGNet extract feature image train Multilayer Perceptron MLP set synthetic image sun09 dataset extract spatial relation MLP predicts spatial relation without bounding box around object space image depicting relation understand spatial relation represented network heatmap overlayed image show region deemed important network Also analyze MLP show relationship activation consistent group node prediction spatial relation show loss group affect network ability identify relation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04092583432793617, 0.046911127865314484, -0.0037572721485048532, 0.0848197489976883, -0.026717787608504295, 0.005975028499960899, 0.010528143495321274, 0.008887460455298424, 0.015241685323417187, -0.03622554987668991, -0.016750892624258995, 0.05479459837079048, -0.003973840735852718, 0.024060949683189392, 0.031492460519075394, -0.09592059999704361, 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696 | 696 | ['Adam R. Kosiorek', 'Alex Bewley', 'Ingmar Posner'] | 1706.09262v2 | Class-agnostic object tracking is particularly difficult in cluttered
environments as target specific discriminative models cannot be learned a
priori. Inspired by how the human visual cortex employs spatial attention and
separate "where" and "what" processing pathways to actively suppress irrelevant
visual features, this work develops a hierarchical attentive recurrent model
for single object tracking in videos. The first layer of attention discards the
majority of background by selecting a region containing the object of interest,
while the subsequent layers tune in on visual features particular to the
tracked object. This framework is fully differentiable and can be trained in a
purely data driven fashion by gradient methods. To improve training
convergence, we augment the loss function with terms for a number of auxiliary
tasks relevant for tracking. Evaluation of the proposed model is performed on
two datasets: pedestrian tracking on the KTH activity recognition dataset and
the more difficult KITTI object tracking dataset. | Hierarchical Attentive Recurrent Tracking | 2,017 | http://arxiv.org/pdf/1706.09262v2 | Title Hierarchical Attentive Recurrent Tracking Summary Classagnostic object tracking particularly difficult cluttered environment target specific discriminative model cannot learned priori Inspired human visual cortex employ spatial attention separate processing pathway actively suppress irrelevant visual feature work develops hierarchical attentive recurrent model single object tracking video first layer attention discard majority background selecting region containing object interest subsequent layer tune visual feature particular tracked object framework fully differentiable trained purely data driven fashion gradient method improve training convergence augment loss function term number auxiliary task relevant tracking Evaluation proposed model performed two datasets pedestrian tracking KTH activity recognition dataset difficult KITTI object tracking dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01399760227650404, 0.014225639402866364, 0.017151782289147377, 0.07335303723812103, 0.02937290258705616, -0.009481535293161869, -0.004146979656070471, -0.015994062647223473, -0.03165788576006889, -0.03880343213677406, 0.004369891248643398, -0.02563496306538582, -0.006857514381408691, 0.0307307206094265, 0.03230918198823929, -0.08862744271755219, -0.003939707763493061, 0.02110210992395878, -0.033294133841991425, 0.05790888890624046, 0.051366034895181656, -0.015129084698855877, 0.040983788669109344, 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697 | 697 | ['Seungkyun Hong', 'Seongchan Kim', 'Minsu Joh', 'Sa-kwang Song'] | 1711.10644v2 | Predicting unseen weather phenomena is an important issue for disaster
management. In this paper, we suggest a model for a convolutional
sequence-to-sequence autoencoder for predicting undiscovered weather situations
from previous satellite images. We also propose a symmetric skip connection
between encoder and decoder modules to produce more comprehensive image
predictions. To examine our model performance, we conducted experiments for
each suggested model to predict future satellite images from historical
satellite images. A specific combination of skip connection and
sequence-to-sequence autoencoder was able to generate closest prediction from
the ground truth image. | PSIque: Next Sequence Prediction of Satellite Images using a
Convolutional Sequence-to-Sequence Network | 2,017 | http://arxiv.org/pdf/1711.10644v2 | Title PSIque Next Sequence Prediction Satellite Images using Convolutional SequencetoSequence Network Summary Predicting unseen weather phenomenon important issue disaster management paper suggest model convolutional sequencetosequence autoencoder predicting undiscovered weather situation previous satellite image also propose symmetric skip connection encoder decoder module produce comprehensive image prediction examine model performance conducted experiment suggested model predict future satellite image historical satellite image specific combination skip connection sequencetosequence autoencoder able generate closest prediction ground truth image Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.018754642456769943, 0.09524159133434296, -0.001475048717111349, 0.04831339046359062, 0.0031933083664625883, 0.010652001947164536, -0.0016888133250176907, -0.025227637961506844, -0.002432959619909525, 0.02826281450688839, 0.0632391944527626, -0.02445962280035019, 0.020399456843733788, 0.09698973596096039, 0.029526859521865845, -0.08901099115610123, 0.04014236852526665, 0.007683932781219482, -0.014587911777198315, 0.0007967465207912028, -0.0017560902051627636, -0.06161316856741905, 0.02388901077210903, 0.027978168800473213, -0.011459281668066978, 0.025240987539291382, -0.057429954409599304, -0.01725281961262226, 0.013180147856473923, -0.08397924900054932, 0.020835142582654953, 0.0469062514603138, 0.06209072098135948, 0.020279578864574432, 2.3096388304111315e-06, 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698 | 698 | ['Wellington Pinheiro dos Santos', 'Ricardo Emmanuel de Souza', 'Plínio B. dos Santos Filho'] | 1712.00712v1 | Alzheimer's disease is the most common cause of dementia, yet hard to
diagnose precisely without invasive techniques, particularly at the onset of
the disease. This work approaches image analysis and classification of
synthetic multispectral images composed by diffusion-weighted magnetic
resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area
and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging
system was used to acquire all images presented. The classification methods are
based on multilayer perceptrons and Kohonen Self-Organized Map classifiers. We
assume the classes of interest can be separated by hyperquadrics. Therefore, a
2-degree polynomial network is used to classify the original image, generating
the ground truth image. The classification results are used to improve the
usual analysis of the apparent diffusion coefficient map. | Evaluation of Alzheimer's Disease by Analysis of MR Images using
Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the
ADC Maps | 2,017 | http://arxiv.org/pdf/1712.00712v1 | Title Evaluation Alzheimers Disease Analysis MR Images using Multilayer Perceptrons Kohonen SOM Classifiers Alternative ADC Maps Summary Alzheimers disease common cause dementia yet hard diagnose precisely without invasive technique particularly onset disease work approach image analysis classification synthetic multispectral image composed diffusionweighted magnetic resonance MR cerebral image evaluation cerebrospinal fluid area measuring advance Alzheimers disease clinical 15 MR imaging system used acquire image presented classification method based multilayer perceptrons Kohonen SelfOrganized Map classifier assume class interest separated hyperquadrics Therefore 2degree polynomial network used classify original image generating ground truth image classification result used improve usual analysis apparent diffusion coefficient map Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04594212397933006, 0.01745085045695305, -0.030492210760712624, 0.013584626838564873, 0.01706668548285961, 0.012701579369604588, 0.02677692100405693, 0.022421682253479958, 0.09287978708744049, 0.026735689491033554, 0.018283044919371605, 0.016190290451049805, 0.02528703771531582, 0.01034840289503336, 0.03909936919808388, -0.02712489664554596, -0.024665867909789085, 0.019033318385481834, 0.022487906739115715, -0.02040003053843975, -0.01826302893459797, -0.06269478052854538, -0.021220572292804718, 0.007425766903907061, -0.030553579330444336, -0.03442702069878578, 0.056777749210596085, -0.052589576691389084, 0.02562575973570347, 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699 | 699 | ['Cheston Tan', 'Tomaso Poggio'] | 1406.3793v1 | Faces are a class of visual stimuli with unique significance, for a variety
of reasons. They are ubiquitous throughout the course of a person's life, and
face recognition is crucial for daily social interaction. Faces are also unlike
any other stimulus class in terms of certain physical stimulus characteristics.
Furthermore, faces have been empirically found to elicit certain characteristic
behavioral phenomena, which are widely held to be evidence of "holistic"
processing of faces. However, little is known about the neural mechanisms
underlying such holistic face processing. In other words, for the processing of
faces by the primate visual system, the input and output characteristics are
relatively well known, but the internal neural computations are not. The main
aim of this work is to further the fundamental understanding of what causes the
visual processing of faces to be different from that of objects. In this
computational modeling work, we show that a single factor - "neural tuning
size" - is able to account for three key phenomena that are characteristic of
face processing, namely the Composite Face Effect (CFE), Face Inversion Effect
(FIE) and Whole-Part Effect (WPE). Our computational proof-of-principle
provides specific neural tuning properties that correspond to the
poorly-understood notion of holistic face processing, and connects these neural
properties to psychophysical behavior. Overall, our work provides a unified and
parsimonious theoretical account for the disparate empirical data on
face-specific processing, deepening the fundamental understanding of face
processing. | Neural tuning size is a key factor underlying holistic face processing | 2,014 | http://arxiv.org/pdf/1406.3793v1 | Title Neural tuning size key factor underlying holistic face processing Summary Faces class visual stimulus unique significance variety reason ubiquitous throughout course person life face recognition crucial daily social interaction Faces also unlike stimulus class term certain physical stimulus characteristic Furthermore face empirically found elicit certain characteristic behavioral phenomenon widely held evidence holistic processing face However little known neural mechanism underlying holistic face processing word processing face primate visual system input output characteristic relatively well known internal neural computation main aim work fundamental understanding cause visual processing face different object computational modeling work show single factor neural tuning size able account three key phenomenon characteristic face processing namely Composite Face Effect CFE Face Inversion Effect FIE WholePart Effect WPE computational proofofprinciple provides specific neural tuning property correspond poorlyunderstood notion holistic face processing connects neural property psychophysical behavior Overall work provides unified parsimonious theoretical account disparate empirical data facespecific processing deepening fundamental understanding face processing Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.016153737902641296, -0.029728543013334274, -0.04741014167666435, 0.016164788976311684, 0.02687034383416176, 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Subsets and Splits