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2006.01387
|
A combinatorial conjecture from PAC-Bayesian machine learning
|
We present a proof of a combinatorial conjecture from the second author's Ph.D. thesis. The proof relies on binomial and multinomial sums identities. We also discuss the relevance of the conjecture in the context of PAC-Bayesian machine learning.
|
http://arxiv.org/pdf/2006.01387v2
|
[
"M. Younsi",
"A. Lacasse"
] |
2020-06-04T21:02:29Z
|
2020-06-02T04:36:50Z
|
2006.05311
|
Deep learning of free boundary and Stefan problems
|
Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class of forward and inverse free boundary problems called Stefan problems. Specifically, we approximate the unknown solution as well as any moving boundaries by two deep neural networks. Besides, we formulate a new type of inverse Stefan problems that aim to reconstruct the solution and free boundaries directly from sparse and noisy measurements. We demonstrate the effectiveness of our approach in a series of benchmarks spanning different types of Stefan problems, and illustrate how the proposed framework can accurately recover solutions of partial differential equations with moving boundaries and dynamic interfaces. All code and data accompanying this manuscript are publicly available at url{https://github.com/PredictiveIntelligenceLab/DeepStefan}.
|
http://arxiv.org/abs/2006.05311v1
|
[
"Sifan Wang",
"Paris Perdikaris"
] |
2020-06-04T21:14:15Z
|
2020-06-04T21:14:15Z
|
2006.03156
|
Unsupervised clustering of Roman pottery profiles from their SSAE
representation
|
In this paper we introduce the ROman COmmonware POTtery (ROCOPOT) database, which comprises of more than 2000 black and white imaging profiles of pottery shapes extracted from 11 Roman catalogues and related to different excavation sites. The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem: profile similarities are thus explored via the hierarchical clustering of non-linear features learned in the latent representation space of a stacked sparse autoencoder (SSAE) network, unveiling new profile matches. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in the respective communities.
|
http://arxiv.org/pdf/2006.03156v1
|
[
"Simone Parisotto",
"Alessandro Launaro",
"Ninetta Leone",
"Carola-Bibiane Schönlieb"
] |
2020-06-04T22:19:22Z
|
2020-06-04T22:19:22Z
|
2006.01770
|
What's Sex Got To Do With Fair Machine Learning?
|
Debate about fairness in machine learning has largely centered around competing definitions of what fairness or nondiscrimination between groups requires. However, little attention has been paid to what precisely a group is. Many recent approaches to "fairness" require one to specify a causal model of the data generating process. These exercises make an implicit ontological assumption that a racial or sex group is simply a collection of individuals who share a given trait. We show this by exploring the formal assumption of modularity in causal models, which holds that the dependencies captured by one causal pathway are invariant to interventions on any other pathways. Causal models of sex propose two substantive claims: 1) There exists a feature, sex-on-its-own, that is an inherent trait of an individual that causally brings about social phenomena external to it in the world; and 2) the relations between sex and its effects can be modified in whichever ways and the former feature would still retain the meaning that sex has in our world. We argue that this ontological picture is false. Many of the "effects" that sex purportedly "causes" are in fact constitutive features of sex as a social status. They give the social meaning of sex features, meanings that are precisely what make sex discrimination a distinctively morally problematic type of action. Correcting this conceptual error has a number of implications for how models can be used to detect discrimination. Formal diagrams of constitutive relations present an entirely different path toward reasoning about discrimination. Whereas causal diagrams guide the construction of sophisticated modular counterfactuals, constitutive diagrams identify a different kind of counterfactual as central to an inquiry on discrimination: one that asks how the social meaning of a group would be changed if its non-modular features were altered.
|
http://arxiv.org/abs/2006.01770v2
|
[
"Lily Hu",
"Issa Kohler-Hausmann"
] |
2020-06-04T22:54:18Z
|
2020-06-02T16:51:39Z
|
2006.03167
|
Inject Machine Learning into Significance Test for Misspecified Linear
Models
|
Due to its strong interpretability, linear regression is widely used in social science, from which significance test provides the significance level of models or coefficients in the traditional statistical inference. However, linear regression methods rely on the linear assumptions of the ground truth function, which do not necessarily hold in practice. As a result, even for simple non-linear cases, linear regression may fail to report the correct significance level. In this paper, we present a simple and effective assumption-free method for linear approximation in both linear and non-linear scenarios. First, we apply a machine learning method to fit the ground truth function on the training set and calculate its linear approximation. Afterward, we get the estimator by adding adjustments based on the validation set. We prove the concentration inequalities and asymptotic properties of our estimator, which leads to the corresponding significance test. Experimental results show that our estimator significantly outperforms linear regression for non-linear ground truth functions, indicating that our estimator might be a better tool for the significance test.
|
http://arxiv.org/pdf/2006.03167v1
|
[
"Jiaye Teng",
"Yang Yuan"
] |
2020-06-04T23:22:04Z
|
2020-06-04T23:22:04Z
|
2006.03189
|
Human or Machine: Automating Human Likeliness Evaluation of NLG Texts
|
Automatic evaluation of various text quality criteria produced by data-driven intelligent methods is very common and useful because it is cheap, fast, and usually yields repeatable results. In this paper, we present an attempt to automate the human likeliness evaluation of the output text samples coming from natural language generation methods used to solve several tasks. We propose to use a human likeliness score that shows the percentage of the output samples from a method that look as if they were written by a human. Instead of having human participants label or rate those samples, we completely automate the process by using a discrimination procedure based on large pretrained language models and their probability distributions. As follow up, we plan to perform an empirical analysis of human-written and machine-generated texts to find the optimal setup of this evaluation approach. A validation procedure involving human participants will also check how the automatic evaluation correlates with human judgments.
|
http://arxiv.org/pdf/2006.03189v1
|
[
"Erion Çano",
"Ondřej Bojar"
] |
2020-06-05T00:57:52Z
|
2020-06-05T00:57:52Z
|
2006.03193
|
LSTM-based Anomaly Detection for Non-linear Dynamical System
|
Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM) to capture complex temporal changes of the time sequence and make multi-step predictions. Specifically, we first present the framework of LSTM-based anomaly detection in non-linear dynamical system, including data preprocessing, multi-step prediction and anomaly detection. According to the prediction requirement, two types of training modes are explored in multi-step prediction, where samples in a wall shear stress dataset are collected by an adaptive sliding window. On the basis of the multi-step prediction result, a Local Average with Adaptive Parameters (LAAP) algorithm is proposed to extract local numerical features of the time sequence and estimate the upcoming anomaly. The experimental results show that our proposed multi-step prediction method can achieve a higher prediction accuracy than traditional method in wall shear stress dataset, and the LAAP algorithm performs better than the absolute value-based method in anomaly detection task.
|
http://arxiv.org/pdf/2006.03193v1
|
[
"Yue Tan",
"Chunjing Hu",
"Kuan Zhang",
"Kan Zheng",
"Ethan A. Davis",
"Jae Sung Park"
] |
2020-06-05T01:09:36Z
|
2020-06-05T01:09:36Z
|
2006.03199
|
Scene Image Representation by Foreground, Background and Hybrid Features
|
Previous methods for representing scene images based on deep learning primarily consider either the foreground or background information as the discriminating clues for the classification task. However, scene images also require additional information (hybrid) to cope with the inter-class similarity and intra-class variation problems. In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images. We suppose that these three types of information could jointly help to represent scene image more accurately. To this end, we adopt three VGG-16 architectures pre-trained on ImageNet, Places, and Hybrid (both ImageNet and Places) datasets for the corresponding extraction of foreground, background and hybrid information. All these three types of deep features are further aggregated to achieve our final features for the representation of scene images. Extensive experiments on two large benchmark scene datasets (MIT-67 and SUN-397) show that our method produces the state-of-the-art classification performance.
|
http://arxiv.org/abs/2006.03199v1
|
[
"Chiranjibi Sitaula",
"Yong Xiang",
"Sunil Aryal",
"Xuequan Lu"
] |
2020-06-05T01:55:24Z
|
2020-06-05T01:55:24Z
|
2005.03776
|
Mapping Natural Language Instructions to Mobile UI Action Sequences
|
We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PIXELHELP, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in HowTo instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PIXELHELP.
|
http://arxiv.org/pdf/2005.03776v2
|
[
"Yang Li",
"Jiacong He",
"Xin Zhou",
"Yuan Zhang",
"Jason Baldridge"
] |
2020-06-05T02:11:56Z
|
2020-05-07T21:41:40Z
|
2006.03210
|
Sentence Compression as Deletion with Contextual Embeddings
|
Sentence compression is the task of creating a shorter version of an input sentence while keeping important information. In this paper, we extend the task of compression by deletion with the use of contextual embeddings. Different from prior work usually using non-contextual embeddings (Glove or Word2Vec), we exploit contextual embeddings that enable our model capturing the context of inputs. More precisely, we utilize contextual embeddings stacked by bidirectional Long-short Term Memory and Conditional Random Fields for dealing with sequence labeling. Experimental results on a benchmark Google dataset show that by utilizing contextual embeddings, our model achieves a new state-of-the-art F-score compared to strong methods reported on the leader board.
|
http://arxiv.org/pdf/2006.03210v1
|
[
"Minh-Tien Nguyen",
"Bui Cong Minh",
"Dung Tien Le",
"Le Thai Linh"
] |
2020-06-05T02:40:46Z
|
2020-06-05T02:40:46Z
|
2002.05645
|
Training Large Neural Networks with Constant Memory using a New
Execution Algorithm
|
Widely popular transformer-based NLP models such as BERT and Turing-NLG have enormous capacity trending to billions of parameters. Current execution methods demand brute-force resources such as HBM devices and high speed interconnectivity for data parallelism. In this paper, we introduce a new relay-style execution technique called L2L (layer-to-layer) where at any given moment, the device memory is primarily populated only with the executing layer(s)'s footprint. The model resides in the DRAM memory attached to either a CPU or an FPGA as an entity we call eager param-server (EPS). To overcome the bandwidth issues of shuttling parameters to and from EPS, the model is executed a layer at a time across many micro-batches instead of the conventional method of minibatches over whole model. L2L is implemented using 16GB V100 devices for BERT-Large running it with a device batch size of up to 256. Our results show 45% reduction in memory and 40% increase in the throughput compared to the state-of-the-art baseline. L2L is also able to fit models up to 50 Billion parameters on a machine with a single 16GB V100 and 512GB CPU memory and without requiring any model partitioning. L2L scales to arbitrary depth allowing researchers to develop on affordable devices which is a big step toward democratizing AI. By running the optimizer in the host EPS, we show a new form of mixed precision for faster throughput and convergence. In addition, the EPS enables dynamic neural architecture approaches by varying layers across iterations. Finally, we also propose and demonstrate a constant memory variation of L2L and we propose future enhancements. This work has been performed on GPUs first, but also targeted towards all high TFLOPS/Watt accelerators.
|
http://arxiv.org/pdf/2002.05645v5
|
[
"Bharadwaj Pudipeddi",
"Maral Mesmakhosroshahi",
"Jinwen Xi",
"Sujeeth Bharadwaj"
] |
2020-06-05T03:00:26Z
|
2020-02-13T17:29:47Z
|
1904.08064
|
Forecasting with time series imaging
|
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset.
|
http://arxiv.org/abs/1904.08064v3
|
[
"Xixi Li",
"Yanfei Kang",
"Feng Li"
] |
2020-06-05T03:13:12Z
|
2019-04-17T03:18:45Z
|
2006.03221
|
Evaluating Text Coherence at Sentence and Paragraph Levels
|
In this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering. We collected four distinct corpora from different domains on which we investigate the adaptation of existing sentence ordering methods to a paragraph ordering task. We also compare the learnability and robustness of existing models by artificially creating mini datasets and noisy datasets respectively and verifying the efficiency of established models under these circumstances. Furthermore, we carry out human evaluation on the rearranged passages from two competitive models and confirm that WLCS-l is a better metric performing significantly higher correlations with human rating than tau, the most prevalent metric used before. Results from these evaluations show that except for certain extreme conditions, the recurrent graph neural network-based model is an optimal choice for coherence modeling.
|
http://arxiv.org/pdf/2006.03221v1
|
[
"Sennan Liu",
"Shuang Zeng",
"Sujian Li"
] |
2020-06-05T03:31:49Z
|
2020-06-05T03:31:49Z
|
2006.02648
|
Experiments on Paraphrase Identification Using Quora Question Pairs
Dataset
|
We modeled the Quora question pairs dataset to identify a similar question. The dataset that we use is provided by Quora. The task is a binary classification. We tried several methods and algorithms and different approach from previous works. For feature extraction, we used Bag of Words including Count Vectorizer, and Term Frequency-Inverse Document Frequency with unigram for XGBoost and CatBoost. Furthermore, we also experimented with WordPiece tokenizer which improves the model performance significantly. We achieved up to 97 percent accuracy. Code and Dataset.
|
http://arxiv.org/pdf/2006.02648v2
|
[
"Andreas Chandra",
"Ruben Stefanus"
] |
2020-06-05T03:38:51Z
|
2020-06-04T05:43:25Z
|
1912.01809
|
Deep Fictitious Play for Finding Markovian Nash Equilibrium in
Multi-Agent Games
|
We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games. Following the idea of fictitious play, we recast the $N$-player game into $N$ decoupled decision problems (one for each player) and solve them iteratively. The individual decision problem is characterized by a semilinear Hamilton-Jacobi-Bellman equation, to solve which we employ the recently developed deep BSDE method. The resulted algorithm can solve large $N$-player games for which conventional numerical methods would suffer from the curse of dimensionality. Multiple numerical examples involving identical or heterogeneous agents, with risk-neutral or risk-sensitive objectives, are tested to validate the accuracy of the proposed algorithm in large group games. Even for a fifty-player game with the presence of common noise, the proposed algorithm still finds the approximate Nash equilibrium accurately, which, to our best knowledge, is difficult to achieve by other numerical algorithms.
|
http://arxiv.org/pdf/1912.01809v2
|
[
"Jiequn Han",
"Ruimeng Hu"
] |
2020-06-05T03:59:07Z
|
2019-12-04T05:55:03Z
|
1909.06717
|
Adversarial Partial Multi-Label Learning
|
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial multi-label learning. The PML-GAN model uses a disambiguation network to identify noisy labels and uses a multi-label prediction network to map the training instances to the disambiguated label vectors, while deploying a generative adversarial network as an inverse mapping from label vectors to data samples in the input feature space. The learning of the overall model corresponds to a minimax adversarial game, which enhances the correspondence of input features with the output labels in a bi-directional mapping. Extensive experiments are conducted on multiple datasets, while the proposed model demonstrates the state-of-the-art performance for partial multi-label learning.
|
http://arxiv.org/pdf/1909.06717v2
|
[
"Yan Yan",
"Yuhong Guo"
] |
2020-06-05T04:11:26Z
|
2019-09-15T02:34:07Z
|
2006.08338
|
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant
Recognition in Biomedical Literature
|
We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches in such tasks heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.
|
http://arxiv.org/pdf/2006.08338v1
|
[
"Chaoran Cheng",
"Fei Tan",
"Zhi Wei"
] |
2020-06-05T04:39:34Z
|
2020-06-05T04:39:34Z
|
2006.03230
|
Continuous Transfer Learning with Label-informed Distribution Alignment
|
Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little is devoted to modeling the time evolving target domain, such as the online reviews for movies. To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain. One major challenge associated with continuous transfer learning is the potential occurrence of negative transfer as the target domain evolves over time. To address this challenge, we propose a novel label-informed C-divergence between the source and target domains in order to measure the shift of data distributions as well as to identify potential negative transfer. We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence. Furthermore, we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space. In addition, we define a transfer signature for characterizing the negative transfer based on C-divergence, which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios. Extensive experiments on synthetic and real data sets demonstrate the effectiveness of our TransLATE framework.
|
http://arxiv.org/pdf/2006.03230v1
|
[
"Jun Wu",
"Jingrui He"
] |
2020-06-05T04:44:58Z
|
2020-06-05T04:44:58Z
|
2006.03231
|
Parallel ensemble methods for causal direction inference
|
Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science. A causal direction inference algorithm maps the observation data into a binary value which represents either x causes y or y causes x. The nature of these algorithms makes the results unstable with the change of data points. Therefore the accuracy of the causal direction inference can be improved significantly by using parallel ensemble frameworks. In this paper, new causal direction inference algorithms based on several ways of parallel ensemble are proposed. Theoretical analyses on accuracy rates are given. Experiments are done on both of the artificial data sets and the real world data sets. The accuracy performances of the methods and their computational efficiencies in parallel computing environment are demonstrated.
|
http://arxiv.org/pdf/2006.03231v1
|
[
"Yulai Zhang",
"Jiachen Wang",
"Gang Cen",
"Guiming Luo"
] |
2020-06-05T05:07:52Z
|
2020-06-05T05:07:52Z
|
2006.03236
|
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing
|
With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further improve the model capacity. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading comprehension. The code and pretrained checkpoints are available at https://github.com/laiguokun/Funnel-Transformer.
|
http://arxiv.org/pdf/2006.03236v1
|
[
"Zihang Dai",
"Guokun Lai",
"Yiming Yang",
"Quoc V. Le"
] |
2020-06-05T05:16:23Z
|
2020-06-05T05:16:23Z
|
2006.03239
|
Think out of the package: Recommending package types for e-commerce
shipments
|
Multiple product attributes like dimensions, weight, fragility, liquid content etc. determine the package type used by e-commerce companies to ship products. Sub-optimal package types lead to damaged shipments, incurring huge damage related costs and adversely impacting the company's reputation for safe delivery. Items can be shipped in more protective packages to reduce damage costs, however this increases the shipment costs due to expensive packaging and higher transportation costs. In this work, we propose a multi-stage approach that trades-off between shipment and damage costs for each product, and accurately assigns the optimal package type using a scalable, computationally efficient linear time algorithm. A simple binary search algorithm is presented to find the hyper-parameter that balances between the shipment and damage costs. Our approach when applied to choosing package type for Amazon shipments, leads to significant cost savings of tens of millions of dollars in emerging marketplaces, by decreasing both the overall shipment cost and the number of in-transit damages. Our algorithm is live and deployed in the production system where, package types for more than 130,000 products have been modified based on the model's recommendation, realizing a reduction in damage rate of 24%.
|
http://arxiv.org/pdf/2006.03239v1
|
[
"Karthik S. Gurumoorthy",
"Subhajit Sanyal",
"Vineet Chaoji"
] |
2020-06-05T05:27:51Z
|
2020-06-05T05:27:51Z
|
2006.03241
|
Bayesian Sparse Covariance Structure Analysis for Correlated Count Data
|
In this paper, we propose a Bayesian Graphical LASSO for correlated countable data and apply it to spatial crime data. In the proposed model, we assume a Gaussian Graphical Model for the latent variables which dominate the potential risks of crimes. To evaluate the proposed model, we determine optimal hyperparameters which represent samples better. We apply the proposed model for estimation of the sparse inverse covariance of the latent variable and evaluate the partial correlation coefficients. Finally, we illustrate the results on crime spots data and consider the estimated latent variables and the partial correlation coefficients of the sparse inverse covariance.
|
http://arxiv.org/pdf/2006.03241v1
|
[
"Sho Ichigozaki",
"Takahiro Kawashima",
"Hayaru Shouno"
] |
2020-06-05T05:34:35Z
|
2020-06-05T05:34:35Z
|
2005.02593
|
Learning Architectures from an Extended Search Space for Language
Modeling
|
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In this paper, we extend the search space of NAS. In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS). For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously. We implement our model in a differentiable architecture search system. For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and WikiText data, with a new state-of-the-art on PTB. Moreover, the learned architectures show good transferability to other systems. E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale pre-learned architectures.
|
http://arxiv.org/pdf/2005.02593v2
|
[
"Yinqiao Li",
"Chi Hu",
"Yuhao Zhang",
"Nuo Xu",
"Yufan Jiang",
"Tong Xiao",
"Jingbo Zhu",
"Tongran Liu",
"Changliang Li"
] |
2020-06-05T06:23:49Z
|
2020-05-06T05:02:33Z
|
2006.03257
|
Aspect-based Sentiment Analysis of Scientific Reviews
|
Scientific papers are complex and understanding the usefulness of these papers requires prior knowledge. Peer reviews are comments on a paper provided by designated experts on that field and hold a substantial amount of information, not only for the editors and chairs to make the final decision, but also to judge the potential impact of the paper. In this paper, we propose to use aspect-based sentiment analysis of scientific reviews to be able to extract useful information, which correlates well with the accept/reject decision. While working on a dataset of close to 8k reviews from ICLR, one of the top conferences in the field of machine learning, we use an active learning framework to build a training dataset for aspect prediction, which is further used to obtain the aspects and sentiments for the entire dataset. We show that the distribution of aspect-based sentiments obtained from a review is significantly different for accepted and rejected papers. We use the aspect sentiments from these reviews to make an intriguing observation, certain aspects present in a paper and discussed in the review strongly determine the final recommendation. As a second objective, we quantify the extent of disagreement among the reviewers refereeing a paper. We also investigate the extent of disagreement between the reviewers and the chair and find that the inter-reviewer disagreement may have a link to the disagreement with the chair. One of the most interesting observations from this study is that reviews, where the reviewer score and the aspect sentiments extracted from the review text written by the reviewer are consistent, are also more likely to be concurrent with the chair's decision.
|
http://arxiv.org/abs/2006.03257v1
|
[
"Souvic Chakraborty",
"Pawan Goyal",
"Animesh Mukherjee"
] |
2020-06-05T07:06:01Z
|
2020-06-05T07:06:01Z
|
1910.14315
|
BottleNet++: An End-to-End Approach for Feature Compression in
Device-Edge Co-Inference Systems
|
The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance the on-device computation and the communication overhead, the splitting point needs to be carefully picked, while the intermediate feature needs to be compressed before transmission. Existing studies decoupled the design of model splitting, feature compression, and communication, which may lead to excessive resource consumption of the mobile device. In this paper, we introduce an end-to-end architecture, named BottleNet++, that consists of an encoder, a non-trainable channel layer, and a decoder for more efficient feature compression and transmission. The encoder and decoder essentially implement joint source-channel coding via convolutional neural networks (CNNs), while explicitly considering the effect of channel noise. By exploiting the strong sparsity and the fault-tolerant property of the intermediate feature in a deep neural network (DNN), BottleNet++ achieves a much higher compression ratio than existing methods. Furthermore, by providing the channel condition to the encoder as an input, our method enjoys a strong generalization ability in different channel conditions. Compared with merely transmitting intermediate data without feature compression, BottleNet++ achieves up to 64x bandwidth reduction over the additive white Gaussian noise channel and up to 256x bit compression ratio in the binary erasure channel, with less than 2% reduction in accuracy. With a higher compression ratio, BottleNet++ enables splitting a DNN at earlier layers, which leads to up to 3x reduction in on-device computation compared with other compression methods.
|
http://arxiv.org/pdf/1910.14315v5
|
[
"Jiawei Shao",
"Jun Zhang"
] |
2020-06-05T07:16:49Z
|
2019-10-31T08:58:44Z
|
2006.03274
|
GMAT: Global Memory Augmentation for Transformers
|
Transformer-based models have become ubiquitous in natural language processing thanks to their large capacity, innate parallelism and high performance. The contextualizing component of a Transformer block is the $textit{pairwise dot-product}$ attention that has a large $Omega(L^2)$ memory requirement for length $L$ sequences, limiting its ability to process long documents. This has been the subject of substantial interest recently, where multiple approximations were proposed to reduce the quadratic memory requirement using sparse attention matrices. In this work, we propose to augment sparse Transformer blocks with a dense attention-based $textit{global memory}$ of length $M$ ($ll L$) which provides an aggregate global view of the entire input sequence to each position. Our augmentation has a manageable $O(Mcdot(L+M))$ memory overhead, and can be seamlessly integrated with prior sparse solutions. Moreover, global memory can also be used for sequence compression, by representing a long input sequence with the memory representations only. We empirically show that our method leads to substantial improvement on a range of tasks, including (a) synthetic tasks that require global reasoning, (b) masked language modeling, and (c) reading comprehension.
|
http://arxiv.org/pdf/2006.03274v1
|
[
"Ankit Gupta",
"Jonathan Berant"
] |
2020-06-05T07:50:40Z
|
2020-06-05T07:50:40Z
|
2006.12975
|
A Data Scientist's Guide to Streamflow Prediction
|
In recent years, the paradigms of data-driven science have become essential components of physical sciences, particularly in geophysical disciplines such as climatology. The field of hydrology is one of these disciplines where machine learning and data-driven models have attracted significant attention. This offers significant potential for data scientists' contributions to hydrologic research. As in every interdisciplinary research effort, an initial mutual understanding of the domain is key to successful work later on. In this work, we focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow, the volume of water flowing in a river. This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way. We have captured lessons that we have learned while "coming up to speed" on streamflow prediction and hope that our experiences will be useful to the community.
|
http://arxiv.org/pdf/2006.12975v1
|
[
"Martin Gauch",
"Jimmy Lin"
] |
2020-06-05T08:04:37Z
|
2020-06-05T08:04:37Z
|
2006.03312
|
PLANS: Robust Program Learning from Neurally Inferred Specifications
|
Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in an unsupervised way as they inherently capture logical rules, while neural models are more realistically scalable to raw, high-dimensional input, and provide resistance to noisy I/O specifications. We introduce PLANS (Program LeArning from Neurally inferred Specifications), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O specifications to synthesize a program capturing the different observations. In order to address the key challenge of making PLANS resistant to noise in the network's output, we introduce a filtering heuristic for I/O specifications based on selective classification techniques. We obtain state-of-the-art performance at program synthesis from diverse demonstration videos in the Karel and ViZDoom environments, while requiring no ground-truth program for training. We make our implementation available at github.com/rdang-nhu/PLANS.
|
http://arxiv.org/pdf/2006.03312v1
|
[
"Raphaël Dang-Nhu"
] |
2020-06-05T08:51:34Z
|
2020-06-05T08:51:34Z
|
2006.03315
|
Multi-modal Feature Fusion with Feature Attention for VATEX Captioning
Challenge 2020
|
This report describes our model for VATEX Captioning Challenge 2020. First, to gather information from multiple domains, we extract motion, appearance, semantic and audio features. Then we design a feature attention module to attend on different feature when decoding. We apply two types of decoders, top-down and X-LAN and ensemble these models to get the final result. The proposed method outperforms official baseline with a significant gap. We achieve 76.0 CIDEr and 50.0 CIDEr on English and Chinese private test set. We rank 2nd on both English and Chinese private test leaderboard.
|
http://arxiv.org/pdf/2006.03315v1
|
[
"Ke Lin",
"Zhuoxin Gan",
"Liwei Wang"
] |
2020-06-05T09:00:36Z
|
2020-06-05T09:00:36Z
|
2004.13271
|
Trainable Activation Function in Image Classification
|
In the current research of neural networks, the activation function is manually specified by human and not able to change themselves during training. This paper focus on how to make the activation function trainable for deep neural networks. We use series and linear combination of different activation functions make activation functions continuously variable. Also, we test the performance of CNNs with Fourier series simulated activation(Fourier-CNN) and CNNs with linear combined activation function (LC-CNN) on Cifar-10 dataset. The result shows our trainable activation function reveals better performance than the most used ReLU activation function. Finally, we improves the performance of Fourier-CNN with Autoencoder, and test the performance of PSO algorithm in optimizing the parameters of networks
|
http://arxiv.org/pdf/2004.13271v2
|
[
"Zhaohe Liao"
] |
2020-06-05T09:05:35Z
|
2020-04-28T03:50:53Z
|
2006.03318
|
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN
Training
|
Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks. The efficacy of software-level optimizations can vary significantly when used in different deployment configurations. It is onerous and error-prone for ML practitioners and system developers to implement each optimization separately, and determine which ones will improve performance in their own configurations. Unfortunately, existing profiling tools do not aim to answer predictive questions such as "How will optimization X affect the performance of my model?". We address this critical limitation, and proposes a new profiling tool, Daydream, to help programmers efficiently explore the efficacy of DNN optimizations. Daydream models DNN execution with a fine-grained dependency graph based on low-level traces collected by CUPTI, and predicts runtime by simulating execution based on the dependency graph. Daydream maps the low-level traces using DNN domain-specific knowledge, and introduces a set of graph-transformation primitives that can easily model a wide variety of optimizations. We show that Daydream is able to model most mainstream DNN optimization techniques, and accurately predict the efficacy of optimizations that will result in significant performance improvements.
|
http://arxiv.org/pdf/2006.03318v1
|
[
"Hongyu Zhu",
"Amar Phanishayee",
"Gennady Pekhimenko"
] |
2020-06-05T09:08:16Z
|
2020-06-05T09:08:16Z
|
2005.07666
|
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC)
Resource Scheduling
|
In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We~describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we~introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we~show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We~conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.
|
http://arxiv.org/abs/2005.07666v2
|
[
"Tegg Taekyong Sung",
"Jeongsoo Ha",
"Jeewoo Kim",
"Alex Yahja",
"Chae-Bong Sohn",
"Bo Ryu"
] |
2020-06-05T09:53:01Z
|
2020-05-15T17:31:27Z
|
1908.08526
|
Double Reinforcement Learning for Efficient Off-Policy Evaluation in
Markov Decision Processes
|
Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits of OPE in Markov decision processes (MDPs), where actions, rewards, and states are memoryless. We show existing OPE estimators may fail to be efficient in this setting. We develop a new estimator based on cross-fold estimation of $q$-functions and marginalized density ratios, which we term double reinforcement learning (DRL). We show that DRL is efficient when both components are estimated at fourth-root rates and is also doubly robust when only one component is consistent. We investigate these properties empirically and demonstrate the performance benefits due to harnessing memorylessness.
|
http://arxiv.org/pdf/1908.08526v3
|
[
"Nathan Kallus",
"Masatoshi Uehara"
] |
2020-06-05T09:58:10Z
|
2019-08-22T17:57:19Z
|
1907.04152
|
Interpretable Segmentation of Medical Free-Text Records Based on Word
Embeddings
|
Is it true that patients with similar conditions get similar diagnoses? In this paper we show NLP methods and a unique corpus of documents to validate this claim. We (1) introduce a method for representation of medical visits based on free-text descriptions recorded by doctors, (2) introduce a new method for clustering of patients' visits and (3) present an~application of the proposed method on a corpus of 100,000 visits. With the proposed method we obtained stable and separated segments of visits which were positively validated against final medical diagnoses. We show how the presented algorithm may be used to aid doctors during their practice.
|
http://arxiv.org/pdf/1907.04152v3
|
[
"Adam Gabriel Dobrakowski",
"Agnieszka Mykowiecka",
"Małgorzata Marciniak",
"Wojciech Jaworski",
"Przemysław Biecek"
] |
2020-06-05T10:09:48Z
|
2019-07-03T15:22:04Z
|
2006.03349
|
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning
to End
|
The focus in deep learning research has been mostly to push the limits of prediction accuracy. However, this was often achieved at the cost of increased complexity, raising concerns about the interpretability and the reliability of deep networks. Recently, an increasing attention has been given to untangling the complexity of deep networks and quantifying their uncertainty for different computer vision tasks. Differently, the task of depth completion has not received enough attention despite the inherent noisy nature of depth sensors. In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction. We propose a novel approach to identify disturbed measurements in the input by learning an input confidence estimator in a self-supervised manner based on the normalized convolutional neural networks (NCNNs). Further, we propose a probabilistic version of NCNNs that produces a statistically meaningful uncertainty measure for the final prediction. When we evaluate our approach on the KITTI dataset for depth completion, we outperform all the existing Bayesian Deep Learning approaches in terms of prediction accuracy, quality of the uncertainty measure, and the computational efficiency. Moreover, our small network with 670k parameters performs on-par with conventional approaches with millions of parameters. These results give strong evidence that separating the network into parallel uncertainty and prediction streams leads to state-of-the-art performance with accurate uncertainty estimates.
|
http://arxiv.org/pdf/2006.03349v1
|
[
"Abdelrahman Eldesokey",
"Michael Felsberg",
"Karl Holmquist",
"Mikael Persson"
] |
2020-06-05T10:18:35Z
|
2020-06-05T10:18:35Z
|
2006.03350
|
Concurrent Decentralized Channel Allocation and Access Point Selection
using Multi-Armed Bandits in multi BSS WLANs
|
Enterprise Wireless Local Area Networks (WLANs) consist of multiple Access Points (APs) covering a given area. Finding a suitable network configuration able to maximize the performance of enterprise WLANs is a challenging task given the complex dependencies between APs and stations. Recently, in wireless networking, the use of reinforcement learning techniques has emerged as an effective solution to efficiently explore the impact of different network configurations in the system performance, identifying those that provide better performance. In this paper, we study if Multi-Armed Bandits (MABs) are able to offer a feasible solution to the decentralized channel allocation and AP selection problems in Enterprise WLAN scenarios. To do so, we empower APs and stations with agents that, by means of implementing the Thompson sampling algorithm, explore and learn which is the best channel to use, and which is the best AP to associate, respectively. Our evaluation is performed over randomly generated scenarios, which enclose different network topologies and traffic loads. The presented results show that the proposed adaptive framework using MABs outperform the static approach (i.e., using always the initial default configuration, usually random) regardless of the network density and the traffic requirements. Moreover, we show that the use of the proposed framework reduces the performance variability between different scenarios. Results also show that we achieve the same performance (or better) than static strategies with less APs for the same number of stations. Finally, special attention is placed on how the agents interact. Even if the agents operate in a completely independent manner, their decisions have interrelated effects, as they take actions over the same set of channel resources.
|
http://arxiv.org/abs/2006.03350v1
|
[
"Álvaro López-Raventós",
"Boris Bellalta"
] |
2020-06-05T10:20:40Z
|
2020-06-05T10:20:40Z
|
2006.03351
|
Extracting Spatiotemporal Demand for Public Transit from Mobility Data
|
With people constantly migrating to different urban areas, our mobility needs for work, services and leisure are transforming rapidly. The changing urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations or modelling that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variegated demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of approximately 4.6 million daily transit traces from the Greater London region reveals distinct demand profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our method of analysing the spatiotemporal geography of a city can be extended to other urban regions with different modes of public transit.
|
http://arxiv.org/pdf/2006.03351v1
|
[
"Trivik Verma",
"Mikhail Sirenko",
"Itto Kornecki",
"Scott Cunningham",
"Nuno AM Araújo"
] |
2020-06-05T10:21:31Z
|
2020-06-05T10:21:31Z
|
2006.02734
|
Robust Sampling in Deep Learning
|
Deep learning requires regularization mechanisms to reduce overfitting and improve generalization. We address this problem by a new regularization method based on distributional robust optimization. The key idea is to modify the contribution from each sample for tightening the empirical risk bound. During the stochastic training, the selection of samples is done according to their accuracy in such a way that the worst performed samples are the ones that contribute the most in the optimization. We study different scenarios and show the ones where it can make the convergence faster or increase the accuracy.
|
http://arxiv.org/pdf/2006.02734v2
|
[
"Aurora Cobo Aguilera",
"Antonio Artés-Rodríguez",
"Fernando Pérez-Cruz",
"Pablo Martínez Olmos"
] |
2020-06-05T10:37:37Z
|
2020-06-04T09:46:52Z
|
2006.03361
|
Learning to Rank Learning Curves
|
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.
|
http://arxiv.org/pdf/2006.03361v1
|
[
"Martin Wistuba",
"Tejaswini Pedapati"
] |
2020-06-05T10:49:52Z
|
2020-06-05T10:49:52Z
|
2006.03364
|
Structure preserving deep learning
|
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks. There are multiple challenging mathematical problems involved in applying deep learning: most deep learning methods require the solution of hard optimisation problems, and a good understanding of the tradeoff between computational effort, amount of data and model complexity is required to successfully design a deep learning approach for a given problem. A large amount of progress made in deep learning has been based on heuristic explorations, but there is a growing effort to mathematically understand the structure in existing deep learning methods and to systematically design new deep learning methods to preserve certain types of structure in deep learning. In this article, we review a number of these directions: some deep neural networks can be understood as discretisations of dynamical systems, neural networks can be designed to have desirable properties such as invertibility or group equivariance, and new algorithmic frameworks based on conformal Hamiltonian systems and Riemannian manifolds to solve the optimisation problems have been proposed. We conclude our review of each of these topics by discussing some open problems that we consider to be interesting directions for future research.
|
http://arxiv.org/pdf/2006.03364v1
|
[
"Elena Celledoni",
"Matthias J. Ehrhardt",
"Christian Etmann",
"Robert I McLachlan",
"Brynjulf Owren",
"Carola-Bibiane Schönlieb",
"Ferdia Sherry"
] |
2020-06-05T10:59:09Z
|
2020-06-05T10:59:09Z
|
2006.04535
|
Improving k-Means Clustering Performance with Disentangled Internal
Representations
|
Deep clustering algorithms combine representation learning and clustering by jointly optimizing a clustering loss and a non-clustering loss. In such methods, a deep neural network is used for representation learning together with a clustering network. Instead of following this framework to improve clustering performance, we propose a simpler approach of optimizing the entanglement of the learned latent code representation of an autoencoder. We define entanglement as how close pairs of points from the same class or structure are, relative to pairs of points from different classes or structures. To measure the entanglement of data points, we use the soft nearest neighbor loss, and expand it by introducing an annealing temperature factor. Using our proposed approach, the test clustering accuracy was 96.2% on the MNIST dataset, 85.6% on the Fashion-MNIST dataset, and 79.2% on the EMNIST Balanced dataset, outperforming our baseline models.
|
http://arxiv.org/pdf/2006.04535v1
|
[
"Abien Fred Agarap",
"Arnulfo P. Azcarraga"
] |
2020-06-05T11:32:34Z
|
2020-06-05T11:32:34Z
|
2006.03385
|
Utilizing machine learning to prevent water main breaks by understanding
pipeline failure drivers
|
Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year. To achieve this objective, we constructed a detailed picture and understanding of the behaviour of the water pipe network by 1) discovering the underlying drivers of water main breaks, and 2) developing a Machine Learning system to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes, and other environmental factors. The ensuing results open up an avenue for Western Water to identify the priority of pipe renewals
|
http://arxiv.org/pdf/2006.03385v1
|
[
"Dilusha Weeraddana",
"Bin Liang",
"Zhidong Li",
"Yang Wang",
"Fang Chen",
"Livia Bonazzi",
"Dean Phillips",
"Nitin Saxena"
] |
2020-06-05T11:44:02Z
|
2020-06-05T11:44:02Z
|
2006.01174
|
BIMCV COVID-19+: a large annotated dataset of RX and CT images from
COVID-19 patients
|
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerably more reduced number of entities annotated in previous datasets. Images are stored in high resolution and entities are localized with anatomical labels and stored in a Medical Imaging Data Structure (MIDS) format. In addition, 10 images were annotated by a team of radiologists to include semantic segmentation of radiological findings. This first iteration of the database includes 1,380 CX, 885 DX and 163 CT studies from 1,311 COVID-19+ patients. This is, to the best of our knowledge, the largest COVID-19+ dataset of images available in an open format. The dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/bimcv-covid19.
|
http://arxiv.org/pdf/2006.01174v3
|
[
"Maria de la Iglesia Vayá",
"Jose Manuel Saborit",
"Joaquim Angel Montell",
"Antonio Pertusa",
"Aurelia Bustos",
"Miguel Cazorla",
"Joaquin Galant",
"Xavier Barber",
"Domingo Orozco-Beltrán",
"Francisco García-García",
"Marisa Caparrós",
"Germán González",
"Jose María Salinas"
] |
2020-06-05T12:53:43Z
|
2020-06-01T18:06:21Z
|
2006.03416
|
Entropy-Regularized $2$-Wasserstein Distance between Gaussian Measures
|
Gaussian distributions are plentiful in applications dealing in uncertainty quantification and diffusivity. They furthermore stand as important special cases for frameworks providing geometries for probability measures, as the resulting geometry on Gaussians is often expressible in closed-form under the frameworks. In this work, we study the Gaussian geometry under the entropy-regularized 2-Wasserstein distance, by providing closed-form solutions for the distance and interpolations between elements. Furthermore, we provide a fixed-point characterization of a population barycenter when restricted to the manifold of Gaussians, which allows computations through the fixed-point iteration algorithm. As a consequence, the results yield closed-form expressions for the 2-Sinkhorn divergence. As the geometries change by varying the regularization magnitude, we study the limiting cases of vanishing and infinite magnitudes, reconfirming well-known results on the limits of the Sinkhorn divergence. Finally, we illustrate the resulting geometries with a numerical study.
|
http://arxiv.org/pdf/2006.03416v1
|
[
"Anton Mallasto",
"Augusto Gerolin",
"Hà Quang Minh"
] |
2020-06-05T13:18:57Z
|
2020-06-05T13:18:57Z
|
2006.03423
|
Generation of Differentially Private Heterogeneous Electronic Health
Records
|
Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advantages that they can be easily distributed and contain many features useful for e.g. classification problems. What makes EHR data sets different from typical machine learning data sets is that they are often very sparse, due to their high dimensionality, and often contain heterogeneous (mixed) data types. Furthermore, the data sets deal with sensitive information, which limits the distribution of any models learned using them, due to privacy concerns. For these reasons, using EHR data in practice presents a real challenge. In this work, we explore using Generative Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of using these synthetic records in place of existing data sets for downstream classification tasks. We will further explore applying differential privacy (DP) preserving optimization in order to produce DP synthetic EHR data sets, which provide rigorous privacy guarantees, and are therefore shareable and usable in the real world. The performance (measured by AUROC, AUPRC and accuracy) of our model's synthetic, heterogeneous data is very close to the original data set (within 3 - 5% of the baseline) for the non-DP model when tested in a binary classification task. Using strong $(1, 10^{-5})$ DP, our model still produces data useful for machine learning tasks, albeit incurring a roughly 17% performance penalty in our tested classification task. We additionally perform a sub-population analysis and find that our model does not introduce any bias into the synthetic EHR data compared to the baseline in either male/female populations, or the 0-18, 19-50 and 51+ age groups in terms of classification performance for either the non-DP or DP variant.
|
http://arxiv.org/pdf/2006.03423v1
|
[
"Kieran Chin-Cheong",
"Thomas Sutter",
"Julia E. Vogt"
] |
2020-06-05T13:21:46Z
|
2020-06-05T13:21:46Z
|
2006.03445
|
Tensorized Transformer for Dynamical Systems Modeling
|
The identification of nonlinear dynamics from observations is essential for the alignment of the theoretical ideas and experimental data. The last, in turn, is often corrupted by the side effects and noise of different natures, so probabilistic approaches could give a more general picture of the process. At the same time, high-dimensional probabilities modeling is a challenging and data-intensive task. In this paper, we establish a parallel between the dynamical systems modeling and language modeling tasks. We propose a transformer-based model that incorporates geometrical properties of the data and provide an iterative training algorithm allowing the fine-grid approximation of the conditional probabilities of high-dimensional dynamical systems.
|
http://arxiv.org/pdf/2006.03445v1
|
[
"Anna Shalova",
"Ivan Oseledets"
] |
2020-06-05T13:43:37Z
|
2020-06-05T13:43:37Z
|
2006.04570
|
Incorporating Image Gradients as Secondary Input Associated with Input
Image to Improve the Performance of the CNN Model
|
CNN is very popular neural network architecture in modern days. It is primarily most used tool for vision related task to extract the important features from the given image. Moreover, CNN works as a filter to extract the important features using convolutional operation in distinct layers. In existing CNN architectures, to train the network on given input, only single form of given input is fed to the network. In this paper, new architecture has been proposed where given input is passed in more than one form to the network simultaneously by sharing the layers with both forms of input. We incorporate image gradient as second form of the input associated with the original input image and allowing both inputs to flow in the network using same number of parameters to improve the performance of the model for better generalization. The results of the proposed CNN architecture, applying on diverse set of datasets such as MNIST, CIFAR10 and CIFAR100 show superior result compared to the benchmark CNN architecture considering inputs in single form.
|
http://arxiv.org/pdf/2006.04570v1
|
[
"Vijay Pandey",
"Shashi Bhushan Jha"
] |
2020-06-05T14:01:52Z
|
2020-06-05T14:01:52Z
|
2004.04571
|
Learning Bayesian Networks that enable full propagation of evidence
|
This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disjoint subgraphs that do not enable full propagation of evidence. This problem is highly prevalent in cases where the sample size of the input data is low with respect to the dimensionality of the model, which is often the case when working with real data. The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue. The results show that this constraint helps the algorithm to estimate the number of true edges with higher accuracy compared to the state-of-the-art. Out of the 13 algorithms investigated, the results rank SaiyanH 4th in reconstructing the true DAG, with accuracy scores lower by 8.1% (F1), 10.2% (BSF), and 19.5% (SHD) compared to the top ranked algorithm, and higher by 75.5% (F1), 118% (BSF), and 4.3% (SHD) compared to the bottom ranked algorithm. Overall, the results suggest that the proposed algorithm discovers satisfactorily accurate connected DAGs in cases where other algorithms produce multiple disjoint subgraphs that often underfit the true graph.
|
http://arxiv.org/pdf/2004.04571v2
|
[
"Anthony Constantinou"
] |
2020-06-05T14:16:49Z
|
2020-04-09T14:44:11Z
|
2006.03486
|
Segmentation of Surgical Instruments for Minimally-Invasive
Robot-Assisted Procedures Using Generative Deep Neural Networks
|
This work proves that semantic segmentation on minimally invasive surgical instruments can be improved by using training data that has been augmented through domain adaptation. The benefit of this method is twofold. Firstly, it suppresses the need of manually labeling thousands of images by transforming synthetic data into realistic-looking data. To achieve this, a CycleGAN model is used, which transforms a source dataset to approximate the domain distribution of a target dataset. Secondly, this newly generated data with perfect labels is utilized to train a semantic segmentation neural network, U-Net. This method shows generalization capabilities on data with variability regarding its rotation- position- and lighting conditions. Nevertheless, one of the caveats of this approach is that the model is unable to generalize well to other surgical instruments with a different shape from the one used for training. This is driven by the lack of a high variance in the geometric distribution of the training data. Future work will focus on making the model more scale-invariant and able to adapt to other types of surgical instruments previously unseen by the training.
|
http://arxiv.org/pdf/2006.03486v1
|
[
"Iñigo Azqueta-Gavaldon",
"Florian Fröhlich",
"Klaus Strobl",
"Rudolph Triebel"
] |
2020-06-05T14:39:41Z
|
2020-06-05T14:39:41Z
|
2006.03492
|
VALUE: Large Scale Voting-based Automatic Labelling for Urban
Environments
|
This paper presents a simple and robust method for the automatic localisation of static 3D objects in large-scale urban environments. By exploiting the potential to merge a large volume of noisy but accurately localised 2D image data, we achieve superior performance in terms of both robustness and accuracy of the recovered 3D information. The method is based on a simple distributed voting schema which can be fully distributed and parallelised to scale to large-scale scenarios. To evaluate the method we collected city-scale data sets from New York City and San Francisco consisting of almost 400k images spanning the area of 40 km$^2$ and used it to accurately recover the 3D positions of traffic lights. We demonstrate a robust performance and also show that the solution improves in quality over time as the amount of data increases.
|
http://arxiv.org/pdf/2006.03492v1
|
[
"Giacomo Dabisias",
"Emanuele Ruffaldi",
"Hugo Grimmett",
"Peter Ondruska"
] |
2020-06-05T14:47:03Z
|
2020-06-05T14:47:03Z
|
1909.06293
|
ISL: A novel approach for deep exploration
|
In this article we explore an alternative approach to address deep exploration and we introduce the ISL algorithm, which is efficient at performing deep exploration. Similarly to maximum entropy RL, we derive the algorithm by augmenting the traditional RL objective with a novel regularization term. A distinctive feature of our approach is that, as opposed to other works that tackle the problem of deep exploration, in our derivation both the learning equations and the exploration-exploitation strategy are derived in tandem as the solution to a well-posed optimization problem whose minimization leads to the optimal value function. Empirically we show that our method exhibits state of the art performance on a range of challenging deep-exploration benchmarks.
|
http://arxiv.org/pdf/1909.06293v4
|
[
"Lucas Cassano",
"Ali H. Sayed"
] |
2020-06-05T15:10:00Z
|
2019-09-13T15:28:09Z
|
2003.01825
|
Scaling MAP-Elites to Deep Neuroevolution
|
Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits. However, present implementations of MAP-Elites and other QD algorithms seem to be limited to low-dimensional controllers with far fewer parameters than modern deep neural network models. In this paper, we propose to leverage the efficiency of Evolution Strategies (ES) to scale MAP-Elites to high-dimensional controllers parameterized by large neural networks. We design and evaluate a new hybrid algorithm called MAP-Elites with Evolution Strategies (ME-ES) for post-damage recovery in a difficult high-dimensional control task where traditional ME fails. Additionally, we show that ME-ES performs efficient exploration, on par with state-of-the-art exploration algorithms in high-dimensional control tasks with strongly deceptive rewards.
|
http://arxiv.org/abs/2003.01825v3
|
[
"Cédric Colas",
"Joost Huizinga",
"Vashisht Madhavan",
"Jeff Clune"
] |
2020-06-05T15:59:15Z
|
2020-03-03T23:02:37Z
|
1906.10121
|
Metaheuristics optimized feedforward neural networks for efficient stock
price prediction
|
The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day opening and closing prices of the stock indices. This paper proposes the design and implementation of a hybrid symbiotic organisms search trained feedforward neural network model for effective and accurate stock price prediction. The symbiotic organisms search algorithm is used as an efficient optimization technique to train the feedforward neural networks, while the resulting training process is used to build a better stock price prediction model. Furthermore, the study also presents a comparative performance evaluation of three different stock price forecasting models; namely, the particle swarm optimization trained feedforward neural network model, the genetic algorithm trained feedforward neural network model and the well-known ARIMA model. The system developed in support of this study utilizes sixteen stock indices as time series datasets for training and testing purpose. Three statistical evaluation measures are used to compare the results of the implemented models, namely the root mean squared error, the mean absolute percentage error and the mean absolution deviation. The computational results obtained reveal that the symbiotic organisms search trained feedforward neural network model exhibits outstanding predictive performance compared to the other models. However, the performance study shows that the three metaheuristics trained feedforward neural network models have promising predictive competence for solving problems of high dimensional nonlinear time series data, which are difficult to capture by traditional models.
|
http://arxiv.org/pdf/1906.10121v3
|
[
"Bradley J. Pillay",
"Absalom E. Ezugwu"
] |
2020-06-05T16:13:42Z
|
2019-06-23T11:31:52Z
|
2006.03541
|
Sentiment Analysis Based on Deep Learning: A Comparative Study
|
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features
|
http://arxiv.org/abs/2006.03541v1
|
[
"Nhan Cach Dang",
"María N. Moreno-García",
"Fernando De la Prieta"
] |
2020-06-05T16:28:10Z
|
2020-06-05T16:28:10Z
|
2005.13135
|
Permutation Matters: Anisotropic Convolutional Layer for Learning on
Point Clouds
|
It has witnessed a growing demand for efficient representation learning on point clouds in many 3D computer vision applications. Behind the success story of convolutional neural networks (CNNs) is that the data (e.g., images) are Euclidean structured. However, point clouds are irregular and unordered. Various point neural networks have been developed with isotropic filters or using weighting matrices to overcome the structure inconsistency on point clouds. However, isotropic filters or weighting matrices limit the representation power. In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point using dot-product attention according to a set of evenly distributed kernel points on a sphere's surface and performs shared anisotropic filters. In fact, dot product with kernel points is by analogy with the dot-product with keys in Transformer as widely used in natural language processing (NLP). From this perspective, PAI-Conv can be regarded as the transformer for point clouds, which is physically meaningful and is robust to cooperate with the efficient random point sampling method. Comprehensive experiments on point clouds demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks compared to state-of-the-art methods.
|
http://arxiv.org/pdf/2005.13135v2
|
[
"Zhongpai Gao",
"Guangtao Zhai",
"Junchi Yan",
"Xiaokang Yang"
] |
2020-06-05T16:32:43Z
|
2020-05-27T02:42:29Z
|
2005.13149
|
On Mutual Information in Contrastive Learning for Visual Representations
|
In recent years, several unsupervised, "contrastive" learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks. We show that this family of algorithms maximizes a lower bound on the mutual information between two or more "views" of an image where typical views come from a composition of image augmentations. Our bound generalizes the InfoNCE objective to support negative sampling from a restricted region of "difficult" contrasts. We find that the choice of negative samples and views are critical to the success of these algorithms. Reformulating previous learning objectives in terms of mutual information also simplifies and stabilizes them. In practice, our new objectives yield representations that outperform those learned with previous approaches for transfer to classification, bounding box detection, instance segmentation, and keypoint detection. % experiments show that choosing more difficult negative samples results in a stronger representation, outperforming those learned with IR, LA, and CMC in classification, bounding box detection, instance segmentation, and keypoint detection. The mutual information framework provides a unifying comparison of approaches to contrastive learning and uncovers the choices that impact representation learning.
|
http://arxiv.org/pdf/2005.13149v2
|
[
"Mike Wu",
"Chengxu Zhuang",
"Milan Mosse",
"Daniel Yamins",
"Noah Goodman"
] |
2020-06-05T16:39:20Z
|
2020-05-27T04:21:53Z
|
2006.03550
|
The Expected Jacobian Outerproduct: Theory and Empirics
|
The expected gradient outerproduct (EGOP) of an unknown regression function is an operator that arises in the theory of multi-index regression, and is known to recover those directions that are most relevant to predicting the output. However, work on the EGOP, including that on its cheap estimators, is restricted to the regression setting. In this work, we adapt this operator to the multi-class setting, which we dub the expected Jacobian outerproduct (EJOP). Moreover, we propose a simple rough estimator of the EJOP and show that somewhat surprisingly, it remains statistically consistent under mild assumptions. Furthermore, we show that the eigenvalues and eigenspaces also remain consistent. Finally, we show that the estimated EJOP can be used as a metric to yield improvements in real-world non-parametric classification tasks: both by its use as a metric, and also as cheap initialization in metric learning tasks.
|
http://arxiv.org/pdf/2006.03550v1
|
[
"Shubhendu Trivedi",
"J. Wang"
] |
2020-06-05T16:42:09Z
|
2020-06-05T16:42:09Z
|
2006.03560
|
Using an interpretable Machine Learning approach to study the drivers of
International Migration
|
Globally increasing migration pressures call for new modelling approaches in order to design effective policies. It is important to have not only efficient models to predict migration flows but also to understand how specific parameters influence these flows. In this paper, we propose an artificial neural network (ANN) to model international migration. Moreover, we use a technique for interpreting machine learning models, namely Partial Dependence Plots (PDP), to show that one can well study the effects of drivers behind international migration. We train and evaluate the model on a dataset containing annual international bilateral migration from $1960$ to $2010$ from $175$ origin countries to $33$ mainly OECD destinations, along with the main determinants as identified in the migration literature. The experiments carried out confirm that: 1) the ANN model is more efficient w.r.t. a traditional model, and 2) using PDP we are able to gain additional insights on the specific effects of the migration drivers. This approach provides much more information than only using the feature importance information used in previous works.
|
http://arxiv.org/pdf/2006.03560v1
|
[
"Harold Silvère Kiossou",
"Yannik Schenk",
"Frédéric Docquier",
"Vinasetan Ratheil Houndji",
"Siegfried Nijssen",
"Pierre Schaus"
] |
2020-06-05T17:13:13Z
|
2020-06-05T17:13:13Z
|
2006.03564
|
Spoken dialect identification in Twitter using a multi-filter
architecture
|
This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter. Our model outputs either GSW or non-GSW and is not meant to be used as a generic language identifier. Our architecture consists of two independent filters where the first one favors recall, and the second one filter favors precision (both towards GSW). Moreover, we do not use binary models (GSW vs. not-GSW) in our filters but rather a multi-class classifier with GSW being one of the possible labels. Our model reaches F1-score of 0.982 on the test set of the shared task.
|
http://arxiv.org/pdf/2006.03564v1
|
[
"Mohammadreza Banaei",
"Rémi Lebret",
"Karl Aberer"
] |
2020-06-05T17:19:15Z
|
2020-06-05T17:19:15Z
|
2005.08844
|
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a
Continuous Path from Policy Gradient to Q-Learning
|
Entropy augmented to reward is known to soften the greedy argmax policy to softmax policy. Entropy augmentation is reformulated and leads to a motivation to introduce an additional entropy term to the objective function in the form of KL-divergence to regularize optimization process. It results in a policy which monotonically improves while interpolating from the current policy to the softmax greedy policy. This policy is used to build a continuously parameterized algorithm which optimize policy and Q-function simultaneously and whose extreme limits correspond to policy gradient and Q-learning, respectively. Experiments show that there can be a performance gain using an intermediate algorithm.
|
http://arxiv.org/pdf/2005.08844v2
|
[
"Donghoon Lee"
] |
2020-06-05T17:21:40Z
|
2020-05-18T16:15:44Z
|
1911.06904
|
Improving Graph Neural Network Representations of Logical Formulae with
Subgraph Pooling
|
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. In particular, there has been a growing interest in adapting structure-aware neural methods to work with the underlying graph representations of logical expressions. While more effective than character and token-level approaches, graph-based methods have often made representational trade-offs that limited their ability to capture key structural properties of their inputs. In this work we propose a novel approach for embedding logical formulae that is designed to overcome the representational limitations of prior approaches. Our architecture works for logics of different expressivity; e.g., first-order and higher-order logic. We evaluate our approach on two standard datasets and show that the proposed architecture achieves state-of-the-art performance on both premise selection and proof step classification.
|
http://arxiv.org/pdf/1911.06904v3
|
[
"Maxwell Crouse",
"Ibrahim Abdelaziz",
"Cristina Cornelio",
"Veronika Thost",
"Lingfei Wu",
"Kenneth Forbus",
"Achille Fokoue"
] |
2020-06-05T17:24:50Z
|
2019-11-15T23:12:30Z
|
2001.10570
|
Detecting Troll Behavior via Inverse Reinforcement Learning: A Case
Study of Russian Trolls in the 2016 US Election
|
Since the 2016 US Presidential election, social media abuse has been eliciting massive concern in the academic community and beyond. Preventing and limiting the malicious activity of users, such as trolls and bots, in their manipulation campaigns is of paramount importance for the integrity of democracy, public health, and more. However, the automated detection of troll accounts is an open challenge. In this work, we propose an approach based on Inverse Reinforcement Learning (IRL) to capture troll behavior and identify troll accounts. We employ IRL to infer a set of online incentives that may steer user behavior, which in turn highlights behavioral differences between troll and non-troll accounts, enabling their accurate classification. As a study case, we consider the troll accounts identified by the US Congress during the investigation of Russian meddling in the 2016 US Presidential election. We report promising results: the IRL-based approach is able to accurately detect troll accounts (AUC=89.1%). The differences in the predictive features between the two classes of accounts enables a principled understanding of the distinctive behaviors reflecting the incentives trolls and non-trolls respond to.
|
http://arxiv.org/pdf/2001.10570v3
|
[
"Luca Luceri",
"Silvia Giordano",
"Emilio Ferrara"
] |
2020-06-05T17:43:28Z
|
2020-01-28T19:50:19Z
|
2006.05267
|
Quantum Criticism: A Tagged News Corpus Analysed for Sentiment and Named
Entities
|
In this research, we continuously collect data from the RSS feeds of traditional news sources. We apply several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of each implementation. We also perform sentiment analysis of each news article at the document, paragraph and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the public through a web interface. Finally, we show how the data in this corpus could be used to identify bias in news reporting.
|
http://arxiv.org/pdf/2006.05267v1
|
[
"Ashwini Badgujar",
"Sheng Chen",
"Andrew Wang",
"Kai Yu",
"Paul Intrevado",
"David Guy Brizan"
] |
2020-06-05T17:59:12Z
|
2020-06-05T17:59:12Z
|
2002.01775
|
Feature-map-level Online Adversarial Knowledge Distillation
|
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. Thus in this paper, we propose an online knowledge distillation method that transfers not only the knowledge of the class probabilities but also that of the feature map using the adversarial training framework. We train multiple networks simultaneously by employing discriminators to distinguish the feature map distributions of different networks. Each network has its corresponding discriminator which discriminates the feature map from its own as fake while classifying that of the other network as real. By training a network to fool the corresponding discriminator, it can learn the other network's feature map distribution. We show that our method performs better than the conventional direct alignment method such as L1 and is more suitable for online distillation. Also, we propose a novel cyclic learning scheme for training more than two networks together. We have applied our method to various network architectures on the classification task and discovered a significant improvement of performance especially in the case of training a pair of a small network and a large one.
|
http://arxiv.org/pdf/2002.01775v3
|
[
"Inseop Chung",
"SeongUk Park",
"Jangho Kim",
"Nojun Kwak"
] |
2020-06-05T18:15:40Z
|
2020-02-05T13:16:37Z
|
2006.03629
|
Hierarchical Class-Based Curriculum Loss
|
Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy. Utilizing this relation can help develop a model capable of satisfying the dependencies and improving model accuracy and interpretability. Further, as different levels in the hierarchy correspond to different granularities, penalizing each label equally can be detrimental to model learning. In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. We theoretically show that the proposed loss function is a tighter bound of 0-1 loss compared to any other loss satisfying the hierarchical constraints. We test our loss function on real world image data sets, and show that it significantly substantially outperforms multiple baselines.
|
http://arxiv.org/pdf/2006.03629v1
|
[
"Palash Goyal",
"Shalini Ghosh"
] |
2020-06-05T18:48:57Z
|
2020-06-05T18:48:57Z
|
2006.03632
|
Rate-adaptive model selection over a collection of black-box contextual
bandit algorithms
|
We consider the model selection task in the stochastic contextual bandit setting. Suppose we are given a collection of base contextual bandit algorithms. We provide a master algorithm that combines them and achieves the same performance, up to constants, as the best base algorithm would, if it had been run on its own. Our approach only requires that each algorithm satisfy a high probability regret bound. Our procedure is very simple and essentially does the following: for a well chosen sequence of probabilities $(p_{t})_{tgeq 1}$, at each round $t$, it either chooses at random which candidate to follow (with probability $p_{t}$) or compares, at the same internal sample size for each candidate, the cumulative reward of each, and selects the one that wins the comparison (with probability $1-p_{t}$). To the best of our knowledge, our proposal is the first one to be rate-adaptive for a collection of general black-box contextual bandit algorithms: it achieves the same regret rate as the best candidate. We demonstrate the effectiveness of our method with simulation studies.
|
http://arxiv.org/pdf/2006.03632v1
|
[
"Aurélien F. Bibaut",
"Antoine Chambaz",
"Mark J. van der Laan"
] |
2020-06-05T18:55:16Z
|
2020-06-05T18:55:16Z
|
2006.03637
|
LDP-Fed: Federated Learning with Local Differential Privacy
|
This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical values, such as click count in Web access logs. However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable. To address this challenge in LDP-Fed, we design and develop two novel approaches. First, LDP-Fed's LDP Module provides a formal differential privacy guarantee for the repeated collection of model training parameters in the federated training of large-scale neural networks over multiple individual participants' private datasets. Second, LDP-Fed implements a suite of selection and filtering techniques for perturbing and sharing select parameter updates with the parameter server. We validate our system deployed with a condensed LDP protocol in training deep neural networks on public data. We compare this version of LDP-Fed, coined CLDP-Fed, with other state-of-the-art approaches with respect to model accuracy, privacy preservation, and system capabilities.
|
http://arxiv.org/pdf/2006.03637v1
|
[
"Stacey Truex",
"Ling Liu",
"Ka-Ho Chow",
"Mehmet Emre Gursoy",
"Wenqi Wei"
] |
2020-06-05T19:15:13Z
|
2020-06-05T19:15:13Z
|
1903.09033
|
Equivariant Entity-Relationship Networks
|
The relational model is a ubiquitous representation of big-data, in part due to its extensive use in databases. In this paper, we propose the Equivariant Entity-Relationship Network (EERN), which is a Multilayer Perceptron equivariant to the symmetry transformations of the Entity-Relationship model. To this end, we identify the most expressive family of linear maps that are exactly equivariant to entity relationship symmetries, and further show that they subsume recently introduced equivariant maps for sets, exchangeable tensors, and graphs. The proposed feed-forward layer has linear complexity in the data and can be used for both inductive and transductive reasoning about relational databases, including database embedding, and the prediction of missing records. This provides a principled theoretical foundation for the application of deep learning to one of the most abundant forms of data. Empirically, EERN outperforms different variants of coupled matrix tensor factorization in both synthetic and real-data experiments.
|
http://arxiv.org/pdf/1903.09033v4
|
[
"Devon Graham",
"Junhao Wang",
"Siamak Ravanbakhsh"
] |
2020-06-05T19:33:06Z
|
2019-03-21T14:42:14Z
|
2006.03646
|
Generating Artificial Outliers in the Absence of Genuine Ones -- a
Survey
|
By definition, outliers are rarely observed in reality, making them difficult to detect or analyse. Artificial outliers approximate such genuine outliers and can, for instance, help with the detection of genuine outliers or with benchmarking outlier-detection algorithms. The literature features different approaches to generate artificial outliers. However, systematic comparison of these approaches remains absent. This surveys and compares these approaches. We start by clarifying the terminology in the field, which varies from publication to publication, and we propose a general problem formulation. Our description of the connection of generating outliers to other research fields like experimental design or generative models frames the field of artificial outliers. Along with offering a concise description, we group the approaches by their general concepts and how they make use of genuine instances. An extensive experimental study reveals the differences between the generation approaches when ultimately being used for outlier detection. This survey shows that the existing approaches already cover a wide range of concepts underlying the generation, but also that the field still has potential for further development. Our experimental study does confirm the expectation that the quality of the generation approaches varies widely, for example, in terms of the data set they are used on. Ultimately, to guide the choice of the generation approach in a specific context, we propose an appropriate general-decision process. In summary, this survey comprises, describes, and connects all relevant work regarding the generation of artificial outliers and may serve as a basis to guide further research in the field.
|
http://arxiv.org/abs/2006.03646v1
|
[
"Georg Steinbuss",
"Klemens Böhm"
] |
2020-06-05T19:33:10Z
|
2020-06-05T19:33:10Z
|
2006.03653
|
Joint learning of variational representations and solvers for inverse
problems with partially-observed data
|
Designing appropriate variational regularization schemes is a crucial part of solving inverse problems, making them better-posed and guaranteeing that the solution of the associated optimization problem satisfies desirable properties. Recently, learning-based strategies have appeared to be very efficient for solving inverse problems, by learning direct inversion schemes or plug-and-play regularizers from available pairs of true states and observations. In this paper, we go a step further and design an end-to-end framework allowing to learn actual variational frameworks for inverse problems in such a supervised setting. The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter. We can jointly learn both components to minimize the data reconstruction error on the true states. This leads to a data-driven discovery of variational models. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r.t. the direct minimization of the variational formulation derived from the known generative model.
|
http://arxiv.org/pdf/2006.03653v1
|
[
"Ronan Fablet",
"Lucas Drumetz",
"Francois Rousseau"
] |
2020-06-05T19:53:34Z
|
2020-06-05T19:53:34Z
|
2005.09047
|
Learning and Inference in Imaginary Noise Models
|
Inspired by recent developments in learning smoothed densities with empirical Bayes, we study variational autoencoders with a decoder that is tailored for the random variable $Y=X+N(0,sigma^2 I_d)$. A notion of smoothed variational inference emerges where the smoothing is implicitly enforced by the noise model of the decoder; "implicit", since during training the encoder only sees clean samples. This is the concept of imaginary noise model, where the noise model dictates the functional form of the variational lower bound $mathcal{L}(sigma)$, but the noisy data are never seen during learning. The model is named $sigma$-VAE. We prove that all $sigma$-VAEs are equivalent to each other via a simple $beta$-VAE expansion: $mathcal{L}(sigma_2) equiv mathcal{L}(sigma_1,beta)$, where $beta=sigma_2^2/sigma_1^2$. We prove a similar result for the Laplace distribution in exponential families. Empirically, we report an intriguing power law $mathcal{D}_{rm KL} sim sigma^{-nu}$ for the learned models and we study the inference in the $sigma$-VAE for unseen noisy data. The experiments were performed on MNIST, where we show that quite remarkably the model can make reasonable inferences on extremely noisy samples even though it has not seen any during training. The vanilla VAE completely breaks down in this regime. We finish with a hypothesis (the XYZ hypothesis) on the findings here.
|
http://arxiv.org/pdf/2005.09047v3
|
[
"Saeed Saremi"
] |
2020-06-05T20:05:03Z
|
2020-05-18T19:38:51Z
|
2006.13852
|
Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and
ARIMA Models
|
Coronavirus disease 2019 (COVID-19) is a global public health crisis that has been declared a pandemic by World Health Organization. Forecasting country-wise COVID-19 cases is necessary to help policymakers and healthcare providers prepare for the future. This study explores the performance of several Long Short-Term Memory (LSTM) models and Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting the number of confirmed COVID-19 cases. Time series of daily cumulative COVID-19 cases were used for generating 1-day, 3-day, and 5-day forecasts using several LSTM models and ARIMA. Two novel k-period performance metrics - k-day Mean Absolute Percentage Error (kMAPE) and k-day Median Symmetric Accuracy (kMdSA) - were developed for evaluating the performance of the models in forecasting time series values for multiple days. Errors in prediction using kMAPE and kMdSA for LSTM models were both as low as 0.05%, while those for ARIMA were 0.07% and 0.06% respectively. LSTM models slightly underestimated while ARIMA slightly overestimated the numbers in the forecasts. The performance of LSTM models is comparable to ARIMA in forecasting COVID-19 cases. While ARIMA requires longer sequences, LSTMs can perform reasonably well with sequence sizes as small as 3. However, LSTMs require a large number of training samples. Further, the development of k-period performance metrics proposed is likely to be useful for performance evaluation of time series models in predicting multiple periods. Based on the k-period performance metrics proposed, both LSTMs and ARIMA are useful for time series analysis and forecasting for COVID-19.
|
http://arxiv.org/pdf/2006.13852v1
|
[
"Arko Barman"
] |
2020-06-05T20:07:48Z
|
2020-06-05T20:07:48Z
|
2006.01974
|
Countering hate on social media: Large scale classification of hate and
counter speech
|
Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage in hate-filled conversations to attempt to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we made use of a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We used an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieved macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97---accuracy in line and even exceeding the state of the art. On thousands of tweets, we used crowdsourcing to verify that the judgments made by the classifier are in close alignment with human judgment. We then used the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.
|
http://arxiv.org/pdf/2006.01974v3
|
[
"Joshua Garland",
"Keyan Ghazi-Zahedi",
"Jean-Gabriel Young",
"Laurent Hébert-Dufresne",
"Mirta Galesic"
] |
2020-06-05T20:38:27Z
|
2020-06-02T23:12:52Z
|
2006.03673
|
Sparse Gaussian Processes via Parametric Families of Compactly-supported
Kernels
|
Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their $O(N^3)$ inference complexity. We propose a method for deriving parametric families of kernel functions with compact spatial support, which yield naturally sparse kernel matrices and enable fast Gaussian process inference via sparse linear algebra. These families generalize known compactly-supported kernel functions, such as the Wendland polynomials. The parameters of this family of kernels can be learned from data using maximum likelihood estimation. Alternatively, we can quickly compute compact approximations of a target kernel using convex optimization. We demonstrate that these approximations incur minimal error over the exact models when modeling data drawn directly from a target GP, and can out-perform the traditional GP kernels on real-world signal reconstruction tasks, while exhibiting sub-quadratic inference complexity.
|
http://arxiv.org/pdf/2006.03673v1
|
[
"Jarred Barber"
] |
2020-06-05T20:44:09Z
|
2020-06-05T20:44:09Z
|
2006.03689
|
Anomaly Detection with Domain Adaptation
|
We study the problem of semi-supervised anomaly detection with domain adaptation. Given a set of normal data from a source domain and a limited amount of normal examples from a target domain, the goal is to have a well-performing anomaly detector in the target domain. We propose the Invariant Representation Anomaly Detection (IRAD) to solve this problem where we first learn to extract a domain-invariant representation. The extraction is achieved by an across-domain encoder trained together with source-specific encoders and generators by adversarial learning. An anomaly detector is then trained using the learnt representations. We evaluate IRAD extensively on digits images datasets (MNIST, USPS and SVHN) and object recognition datasets (Office-Home). Experimental results show that IRAD outperforms baseline models by a wide margin across different datasets. We derive a theoretical lower bound for the joint error that explains the performance decay from overtraining and also an upper bound for the generalization error.
|
http://arxiv.org/pdf/2006.03689v1
|
[
"Ziyi Yang",
"Iman Soltani Bozchalooi",
"Eric Darve"
] |
2020-06-05T21:05:19Z
|
2020-06-05T21:05:19Z
|
2007.13492
|
Self-Supervised Encoder for Fault Prediction in Electrochemical Cells
|
Predicting faults before they occur helps to avoid potential safety hazards. Furthermore, planning the required maintenance actions in advance reduces operation costs. In this article, the focus is on electrochemical cells. In order to predict a cell's fault, the typical approach is to estimate the expected voltage that a healthy cell would present and compare it with the cell's measured voltage in real-time. This approach is possible because, when a fault is about to happen, the cell's measured voltage differs from the one expected for the same operating conditions. However, estimating the expected voltage is challenging, as the voltage of a healthy cell is also affected by its degradation -- an unknown parameter. Expert-defined parametric models are currently used for this estimation task. Instead, we propose the use of a neural network model based on an encoder-decoder architecture. The network receives the operating conditions as input. The encoder's task is to find a faithful representation of the cell's degradation and to pass it to the decoder, which in turn predicts the expected cell's voltage. As no labeled degradation data is given to the network, we consider our approach to be a self-supervised encoder. Results show that we were able to predict the voltage of multiple cells while diminishing the prediction error that was obtained by the parametric models by 53%. This improvement enabled our network to predict a fault 31 hours before it happened, a 64% increase in reaction time compared to the parametric model. Moreover, the output of the encoder can be plotted, adding interpretability to the neural network model.
|
http://arxiv.org/pdf/2007.13492v1
|
[
"Daniel Buades Marcos",
"Soumaya Yacout",
"Said Berriah"
] |
2020-06-05T21:21:36Z
|
2020-06-05T21:21:36Z
|
2006.03701
|
Accelerating Natural Language Understanding in Task-Oriented Dialog
|
Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural language understanding benchmarks. However, these models frequently have in excess of tens of millions of parameters, making them impossible to deploy on-device where resource-efficiency is a major concern. In this work, we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips, with under 100K parameters. Moreover, we perform acceleration experiments on CPUs, where we observe our multi-task model predicts intents and slots nearly 63x faster than even DistilBERT.
|
http://arxiv.org/pdf/2006.03701v1
|
[
"Ojas Ahuja",
"Shrey Desai"
] |
2020-06-05T21:36:33Z
|
2020-06-05T21:36:33Z
|
2006.03713
|
State Action Separable Reinforcement Learning
|
Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulations, Markov Decision Process (MDP) and state-action-value function are the basis for the problem modeling and policy evaluation. However, several challenging issues still remain. Among most cited issues, the enormity of state/action space is an important factor that causes inefficiency in accurately approximating the state-action-value function. We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition. In this regard, we propose a new learning paradigm, State Action Separable Reinforcement Learning (sasRL), wherein the action space is decoupled from the value function learning process for higher efficiency. Then, a light-weight transition model is learned to assist the agent to determine the action that triggers the associated state transition. In addition, our convergence analysis reveals that under certain conditions, the convergence time of sasRL is $O(T^{1/k})$, where $T$ is the convergence time for updating the value function in the MDP-based formulation and $k$ is a weighting factor. Experiments on several gaming scenarios show that sasRL outperforms state-of-the-art MDP-based RL algorithms by up to $75%$.
|
http://arxiv.org/pdf/2006.03713v1
|
[
"Ziyao Zhang",
"Liang Ma",
"Kin K. Leung",
"Konstantinos Poularakis",
"Mudhakar Srivatsa"
] |
2020-06-05T22:02:57Z
|
2020-06-05T22:02:57Z
|
2006.03715
|
Using Stable Matching to Optimize the Balance between Accuracy and
Diversity in Recommendation
|
Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains where it may be desirable to mitigate the popularity bias and to improve the coverage of long-tail items in recommendations given to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items across recommendation lists produced by the system. Unfortunately, attempts to increase aggregate diversity often result in lower recommendation accuracy for end users. Thus, addressing this problem requires an approach that can effectively manage the trade-offs between accuracy and aggregate diversity. In this work, we propose a two-sided post-processing approach in which both user and item utilities are considered. Our goal is to maximize aggregate diversity while minimizing loss in recommendation accuracy. Our solution is a generalization of the Deferred Acceptance algorithm which was proposed as an efficient algorithm to solve the well-known stable matching problem. We prove that our algorithm results in a unique user-optimal stable match between items and users. Using three recommendation datasets, we empirically demonstrate the effectiveness of our approach in comparison to several baselines. In particular, our results show that the proposed solution is quite effective in increasing aggregate diversity and item-side utility while optimizing recommendation accuracy for end users.
|
http://arxiv.org/abs/2006.03715v1
|
[
"Farzad Eskandanian",
"Bamshad Mobasher"
] |
2020-06-05T22:12:25Z
|
2020-06-05T22:12:25Z
|
2006.03729
|
Health Indicator Forecasting for Improving Remaining Useful Life
Estimation
|
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are gaining popularity. One of the most important categories of data-driven approaches relies on a predefined or learned health indicator to characterize the equipment condition up to the present time and make inference on how it is likely to evolve in the future. In these approaches, health indicator forecasting that constructs the health indicator curve over the lifespan using partially observed measurements (i.e., health indicator values within an initial period) plays a key role. Existing health indicator forecasting algorithms, such as the functional Empirical Bayesian approach, the regression-based formulation, a naive scenario matching based on the nearest neighbor, have certain limitations. In this paper, we propose a new `generative + scenario matching' algorithm for health indicator forecasting. The key idea behind the proposed approach is to first non-parametrically fit the underlying health indicator curve with a continuous Gaussian Process using a sample of run-to-failure health indicator curves. The proposed approach then generates a rich set of random curves from the learned distribution, attempting to obtain all possible variations of the target health condition evolution process over the system's lifespan. The health indicator extrapolation for a piece of functioning equipment is inferred as the generated curve that has the highest matching level within the observed period. Our experimental results show the superiority of our algorithm over the other state-of-the-art methods.
|
http://arxiv.org/pdf/2006.03729v1
|
[
"Qiyao Wang",
"Ahmed Farahat",
"Chetan Gupta",
"Haiyan Wang"
] |
2020-06-05T23:02:10Z
|
2020-06-05T23:02:10Z
|
2006.03741
|
Expressivity of expand-and-sparsify representations
|
A simple sparse coding mechanism appears in the sensory systems of several organisms: to a coarse approximation, an input $x in R^d$ is mapped to much higher dimension $m gg d$ by a random linear transformation, and is then sparsified by a winner-take-all process in which only the positions of the top $k$ values are retained, yielding a $k$-sparse vector $z in {0,1}^m$. We study the benefits of this representation for subsequent learning. We first show a universal approximation property, that arbitrary continuous functions of $x$ are well approximated by linear functions of $z$, provided $m$ is large enough. This can be interpreted as saying that $z$ unpacks the information in $x$ and makes it more readily accessible. The linear functions can be specified explicitly and are easy to learn, and we give bounds on how large $m$ needs to be as a function of the input dimension $d$ and the smoothness of the target function. Next, we consider whether the representation is adaptive to manifold structure in the input space. This is highly dependent on the specific method of sparsification: we show that adaptivity is not obtained under the winner-take-all mechanism, but does hold under a slight variant. Finally we consider mappings to the representation space that are random but are attuned to the data distribution, and we give favorable approximation bounds in this setting.
|
http://arxiv.org/pdf/2006.03741v1
|
[
"Sanjoy Dasgupta",
"Christopher Tosh"
] |
2020-06-05T23:36:59Z
|
2020-06-05T23:36:59Z
|
2002.11080
|
The Curious Case of Adversarially Robust Models: More Data Can Help,
Double Descend, or Hurt Generalization
|
Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy. To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize better on the benign/unperturbed test data. In this paper, however, we challenge this conventional belief and show that more training data can hurt the generalization of adversarially robust models in the classification problems. We first investigate the Gaussian mixture classification with a linear loss and identify three regimes based on the strength of the adversary. In the weak adversary regime, more data improves the generalization of adversarially robust models. In the medium adversary regime, with more training data, the generalization loss exhibits a double descent curve, which implies the existence of an intermediate stage where more training data hurts the generalization. In the strong adversary regime, more data almost immediately causes the generalization error to increase. Then we move to the analysis of a two-dimensional classification problem with a 0-1 loss. We prove that more data always hurts the generalization performance of adversarially trained models with large perturbations. To complement our theoretical results, we conduct empirical studies on Gaussian mixture classification, support vector machines (SVMs), and linear regression.
|
http://arxiv.org/pdf/2002.11080v2
|
[
"Yifei Min",
"Lin Chen",
"Amin Karbasi"
] |
2020-06-05T23:46:22Z
|
2020-02-25T18:25:28Z
|
1910.12620
|
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial
Networks
|
Automatic speech recognition (ASR) systems are of vital importance nowadays in commonplace tasks such as speech-to-text processing and language translation. This created the need for an ASR system that can operate in realistic crowded environments. Thus, speech enhancement is a valuable building block in ASR systems and other applications such as hearing aids, smartphones and teleconferencing systems. In this paper, a generative adversarial network (GAN) based framework is investigated for the task of speech enhancement, more specifically speech denoising of audio tracks. A new architecture based on CasNet generator and an additional feature-based loss are incorporated to get realistically denoised speech phonetics. Finally, the proposed framework is shown to outperform other learning and traditional model-based speech enhancement approaches.
|
http://arxiv.org/abs/1910.12620v3
|
[
"Sherif Abdulatif",
"Karim Armanious",
"Karim Guirguis",
"Jayasankar T. Sajeev",
"Bin Yang"
] |
2020-06-06T00:10:35Z
|
2019-10-21T13:27:22Z
|
2002.03844
|
Exploiting Temporal Coherence for Multi-modal Video Categorization
|
Multimodal ML models can process data in multiple modalities (e.g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e.g., object detection, scene understanding). In this paper, we focus on the problem of video categorization by using a multimodal approach. We have developed a novel temporal coherence-based regularization approach, which applies to different types of models (e.g., RNN, NetVLAD, Transformer). We demonstrate through experiments how our proposed multimodal video categorization models with temporal coherence out-perform strong state-of-the-art baseline models.
|
http://arxiv.org/pdf/2002.03844v2
|
[
"Palash Goyal",
"Saurabh Sahu",
"Shalini Ghosh",
"Chul Lee"
] |
2020-06-06T00:17:11Z
|
2020-02-07T06:42:12Z
|
1901.07710
|
Unified estimation framework for unnormalized models with statistical
efficiency
|
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing constants are not explicitly calculated. Although some consistent estimators have been proposed earlier, the problem of statistical efficiency remains. In this study, we propose a unified, statistically efficient estimation framework for unnormalized models and several efficient estimators, whose asymptotic variance is the same as the MLE. The computational cost of these estimators is also reasonable and they can be employed whether the sample space is discrete or continuous. The loss functions of the proposed estimators are derived by combining the following two methods: (1) density-ratio matching using Bregman divergence, and (2) plugging-in nonparametric estimators. We also analyze the properties of the proposed estimators when the unnormalized models are misspecified. The experimental results demonstrate the advantages of our method over existing approaches.
|
http://arxiv.org/pdf/1901.07710v3
|
[
"Masatoshi Uehara",
"Takafumi Kanamori",
"Takashi Takenouchi",
"Takeru Matsuda"
] |
2020-06-06T00:19:01Z
|
2019-01-23T03:40:05Z
|
1908.09257
|
Normalizing Flows: An Introduction and Review of Current Methods
|
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution learning. We aim to provide context and explanation of the models, review current state-of-the-art literature, and identify open questions and promising future directions.
|
http://arxiv.org/abs/1908.09257v4
|
[
"Ivan Kobyzev",
"Simon J. D. Prince",
"Marcus A. Brubaker"
] |
2020-06-06T00:24:11Z
|
2019-08-25T06:14:08Z
|
2003.03501
|
Cross-modal Learning for Multi-modal Video Categorization
|
Multi-modal machine learning (ML) models can process data in multiple modalities (e.g., video, audio, text) and are useful for video content analysis in a variety of problems (e.g., object detection, scene understanding, activity recognition). In this paper, we focus on the problem of video categorization using a multi-modal ML technique. In particular, we have developed a novel multi-modal ML approach that we call "cross-modal learning", where one modality influences another but only when there is correlation between the modalities -- for that, we first train a correlation tower that guides the main multi-modal video categorization tower in the model. We show how this cross-modal principle can be applied to different types of models (e.g., RNN, Transformer, NetVLAD), and demonstrate through experiments how our proposed multi-modal video categorization models with cross-modal learning out-perform strong state-of-the-art baseline models.
|
http://arxiv.org/pdf/2003.03501v3
|
[
"Palash Goyal",
"Saurabh Sahu",
"Shalini Ghosh",
"Chul Lee"
] |
2020-06-06T00:36:52Z
|
2020-03-07T03:21:15Z
|
1910.09086
|
Contextual Prediction Difference Analysis for Explaining Individual
Image Classifications
|
Much effort has been devoted to understanding the decisions of deep neural networks in recent years. A number of model-aware saliency methods were proposed to explain individual classification decisions by creating saliency maps. However, they are not applicable when the parameters and the gradients of the underlying models are unavailable. Recently, model-agnostic methods have also received attention. As one of them, textit{Prediction Difference Analysis} (PDA), a probabilistic sound methodology, was proposed. In this work, we first show that PDA can suffer from saturated classifiers. The saturation phenomenon of classifiers exists widely in current neural network-based classifiers. To explain the decisions of saturated classifiers better, we further propose Contextual PDA, which runs hundreds of times faster than PDA. The experiments show the superiority of our method by explaining image classifications of the state-of-the-art deep convolutional neural networks.
|
http://arxiv.org/pdf/1910.09086v2
|
[
"Jindong Gu",
"Volker Tresp"
] |
2020-06-06T00:41:19Z
|
2019-10-21T00:04:22Z
|
1910.06508
|
Understanding the Curse of Horizon in Off-Policy Evaluation via
Conditional Importance Sampling
|
Off-policy policy estimators that use importance sampling (IS) can suffer from high variance in long-horizon domains, and there has been particular excitement over new IS methods that leverage the structure of Markov decision processes. We analyze the variance of the most popular approaches through the viewpoint of conditional Monte Carlo. Surprisingly, we find that in finite horizon MDPs there is no strict variance reduction of per-decision importance sampling or stationary importance sampling, comparing with vanilla importance sampling. We then provide sufficient conditions under which the per-decision or stationary estimators will provably reduce the variance over importance sampling with finite horizons. For the asymptotic (in terms of horizon $T$) case, we develop upper and lower bounds on the variance of those estimators which yields sufficient conditions under which there exists an exponential v.s. polynomial gap between the variance of importance sampling and that of the per-decision or stationary estimators. These results help advance our understanding of if and when new types of IS estimators will improve the accuracy of off-policy estimation.
|
http://arxiv.org/pdf/1910.06508v2
|
[
"Yao Liu",
"Pierre-Luc Bacon",
"Emma Brunskill"
] |
2020-06-06T01:09:35Z
|
2019-10-15T03:35:30Z
|
2001.08140
|
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine
Translation
|
State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data. As data collection is expensive and infeasible in many cases, domain adaptation methods are needed. In this work, we propose a simple but effect approach to the semi-supervised domain adaptation scenario of NMT, where the aim is to improve the performance of a translation model on the target domain consisting of only non-parallel data with the help of supervised source domain data. This approach iteratively trains a Transformer-based NMT model via three training objectives: language modeling, back-translation, and supervised translation. We evaluate this method on two adaptation settings: adaptation between specific domains and adaptation from a general domain to specific domains, and on two language pairs: German to English and Romanian to English. With substantial performance improvement achieved---up to +19.31 BLEU over the strongest baseline, and +47.69 BLEU improvement over the unadapted model---we present this method as a simple but tough-to-beat baseline in the field of semi-supervised domain adaptation for NMT.
|
http://arxiv.org/pdf/2001.08140v2
|
[
"Di Jin",
"Zhijing Jin",
"Joey Tianyi Zhou",
"Peter Szolovits"
] |
2020-06-06T02:45:10Z
|
2020-01-22T16:42:06Z
|
2005.00116
|
Sequence Information Channel Concatenation for Improving Camera Trap
Image Burst Classification
|
Camera Traps are extensively used to observe wildlife in their natural habitat without disturbing the ecosystem. This could help in the early detection of natural or human threats to animals, and help towards ecological conservation. Currently, a massive number of such camera traps have been deployed at various ecological conservation areas around the world, collecting data for decades, thereby requiring automation to detect images containing animals. Existing systems perform classification to detect if images contain animals by considering a single image. However, due to challenging scenes with animals camouflaged in their natural habitat, it sometimes becomes difficult to identify the presence of animals from merely a single image. We hypothesize that a short burst of images instead of a single image, assuming that the animal moves, makes it much easier for a human as well as a machine to detect the presence of animals. In this work, we explore a variety of approaches, and measure the impact of using short image sequences (burst of 3 images) on improving the camera trap image classification. We show that concatenating masks containing sequence information and the images from the 3-image-burst across channels, improves the ROC AUC by 20% on a test-set from unseen camera-sites, as compared to an equivalent model that learns from a single image.
|
http://arxiv.org/pdf/2005.00116v2
|
[
"Bhuvan Malladihalli Shashidhara",
"Darshan Mehta",
"Yash Kale",
"Dan Morris",
"Megan Hazen"
] |
2020-06-06T02:57:41Z
|
2020-04-30T21:47:14Z
|
2003.12970
|
Elastic Coupled Co-clustering for Single-Cell Genomic Data
|
The recent advances in single-cell technologies have enabled us to profile genomic features at unprecedented resolution and datasets from multiple domains are available, including datasets that profile different types of genomic features and datasets that profile the same type of genomic features across different species. These datasets typically have different powers in identifying the unknown cell types through clustering, and data integration can potentially lead to a better performance of clustering algorithms. In this work, we formulate the problem in an unsupervised transfer learning framework, which utilizes knowledge learned from auxiliary dataset to improve the clustering performance of target dataset. The degree of shared information among the target and auxiliary datasets can vary, and their distributions can also be different. To address these challenges, we propose an elastic coupled co-clustering based transfer learning algorithm, by elastically propagating clustering knowledge obtained from the auxiliary dataset to the target dataset. Implementation on single-cell genomic datasets shows that our algorithm greatly improves clustering performance over the traditional learning algorithms. The source code and data sets are available at https://github.com/cuhklinlab/elasticC3.
|
http://arxiv.org/pdf/2003.12970v2
|
[
"Pengcheng Zeng",
"Zhixiang Lin"
] |
2020-06-06T03:28:35Z
|
2020-03-29T08:21:53Z
|
2002.02302
|
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and
Tighter Regret Bounds for the Non-Episodic Setting
|
We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms for FMDPs. Assuming oracle access to an FMDP planner, they enjoy a Bayesian and a frequentist regret bound respectively, both of which reduce to the near-optimal bound $widetilde{O}(DSsqrt{AT})$ for standard non-factored MDPs. We propose a tighter connectivity measure, factored span, for FMDPs and prove a lower bound that depends on the factored span rather than the diameter $D$. In order to decrease the gap between lower and upper bounds, we propose an adaptation of the REGAL.C algorithm whose regret bound depends on the factored span. Our oracle-efficient algorithms outperform previously proposed near-optimal algorithms on computer network administration simulations.
|
http://arxiv.org/pdf/2002.02302v2
|
[
"Ziping Xu",
"Ambuj Tewari"
] |
2020-06-06T03:30:47Z
|
2020-02-06T15:19:53Z
|
2006.03773
|
Challenges and Thrills of Legal Arguments
|
State-of-the-art attention based models, mostly centered around the transformer architecture, solve the problem of sequence-to-sequence translation using the so-called scaled dot-product attention. While this technique is highly effective for estimating inter-token attention, it does not answer the question of inter-sequence attention when we deal with conversation-like scenarios. We propose an extension, HumBERT, that attempts to perform continuous contextual argument generation using locally trained transformers.
|
http://arxiv.org/pdf/2006.03773v1
|
[
"Anurag Pallaprolu",
"Radha Vaidya",
"Aditya Swaroop Attawar"
] |
2020-06-06T03:43:15Z
|
2020-06-06T03:43:15Z
|
1912.03618
|
Efficient Black-box Assessment of Autonomous Vehicle Safety
|
While autonomous vehicle (AV) technology has shown substantial progress, we still lack tools for rigorous and scalable testing. Real-world testing, the $textit{de-facto}$ evaluation method, is dangerous to the public. Moreover, due to the rare nature of failures, billions of miles of driving are needed to statistically validate performance claims. Thus, the industry has largely turned to simulation to evaluate AV systems. However, having a simulation stack alone is not a solution. A simulation testing framework needs to prioritize which scenarios to run, learn how the chosen scenarios provide coverage of failure modes, and rank failure scenarios in order of importance. We implement a simulation testing framework that evaluates an entire modern AV system as a black box. This framework estimates the probability of accidents under a base distribution governing standard traffic behavior. In order to accelerate rare-event probability evaluation, we efficiently learn to identify and rank failure scenarios via adaptive importance-sampling methods. Using this framework, we conduct the first independent evaluation of a full-stack commercial AV system, Comma AI's OpenPilot.
|
http://arxiv.org/pdf/1912.03618v2
|
[
"Justin Norden",
"Matthew O'Kelly",
"Aman Sinha"
] |
2020-06-06T03:56:29Z
|
2019-12-08T05:12:17Z
|
2001.06545
|
Machine learning and AI-based approaches for bioactive ligand discovery
and GPCR-ligand recognition
|
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
|
http://arxiv.org/abs/2001.06545v3
|
[
"Sebastian Raschka",
"Benjamin Kaufman"
] |
2020-06-06T04:08:39Z
|
2020-01-17T22:01:26Z
|
2006.03776
|
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language
Queries at Phrase Level
|
Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spatial attention networks are known to learn this relationship and focus its gaze on salient objects in the image. Thus, we propose to utilize spatial attention networks for image-level visual-textual fusion preserving local (word) and global (phrase) information to refine region proposals with an in-network Region Proposal Network (RPN) and detect single or multiple regions for a phrase query. We focus only on the phrase query - ground truth pair (referring expression) for a model independent of the constraints of the datasets i.e. additional attributes, context etc. For such referring expression dataset ReferIt game, our Multi-region Attention-assisted Grounding network (MAGNet) achieves over 12% improvement over the state-of-the-art. Without the context from image captions and attribute information in Flickr30k Entities, we still achieve competitive results compared to the state-of-the-art.
|
http://arxiv.org/pdf/2006.03776v1
|
[
"Amar Shrestha",
"Krittaphat Pugdeethosapol",
"Haowen Fang",
"Qinru Qiu"
] |
2020-06-06T04:14:15Z
|
2020-06-06T04:14:15Z
|
2006.03779
|
Chromatic Learning for Sparse Datasets
|
Learning over sparse, high-dimensional data frequently necessitates the use of specialized methods such as the hashing trick. In this work, we design a highly scalable alternative approach that leverages the low degree of feature co-occurrences present in many practical settings. This approach, which we call Chromatic Learning (CL), obtains a low-dimensional dense feature representation by performing graph coloring over the co-occurrence graph of features---an approach previously used as a runtime performance optimization for GBDT training. This color-based dense representation can be combined with additional dense categorical encoding approaches, e.g., submodular feature compression, to further reduce dimensionality. CL exhibits linear parallelizability and consumes memory linear in the size of the co-occurrence graph. By leveraging the structural properties of the co-occurrence graph, CL can compress sparse datasets, such as KDD Cup 2012, that contain over 50M features down to 1024, using an order of magnitude fewer features than frequency-based truncation and the hashing trick while maintaining the same test error for linear models. This compression further enables the use of deep networks in this wide, sparse setting, where CL similarly has favorable performance compared to existing baselines for budgeted input dimension.
|
http://arxiv.org/pdf/2006.03779v1
|
[
"Vladimir Feinberg",
"Peter Bailis"
] |
2020-06-06T04:32:58Z
|
2020-06-06T04:32:58Z
|
2006.12220
|
Learning Diagnosis of COVID-19 from a Single Radiological Image
|
Radiological image is currently adopted as the visual evidence for COVID-19 diagnosis in clinical. Using deep models to realize automated infection measurement and COVID-19 diagnosis is important for faster examination based on radiological imaging. Unfortunately, collecting large training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for COVID-19 diagnosis from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotations of the lung and COVID-19 infection. Our CoSinGAN is able to capture the conditional distribution of visual finds of COVID-19 infection, and further synthesize diverse and high-resolution radiological images that match the input conditions precisely. Both deep classification and segmentation networks trained on synthesized samples from CoSinGAN achieve notable detection accuracy of COVID-19 infection. Such results are significantly better than the counterparts trained on the same extremely small number of real samples (1 or 2 real samples) by using strong data augmentation, and approximate to the counterparts trained on large dataset (2846 real images). It confirms our method can significantly reduce the performance gap between deep models trained on extremely small dataset and on large dataset, and thus has the potential to realize learning COVID-19 diagnosis from few radiological images in the early stage of COVID-19 pandemic. Our codes are made publicly available at https://github.com/PengyiZhang/CoSinGAN.
|
http://arxiv.org/pdf/2006.12220v1
|
[
"Pengyi Zhang",
"Yunxin Zhong",
"Xiaoying Tang",
"Yunlin Deng",
"Xiaoqiong Li"
] |
2020-06-06T07:41:28Z
|
2020-06-06T07:41:28Z
|
1712.06863
|
Pattern recognition techniques for Boson Sampling validation
|
The difficulty of validating large-scale quantum devices, such as Boson Samplers, poses a major challenge for any research program that aims to show quantum advantages over classical hardware. To address this problem, we propose a novel data-driven approach wherein models are trained to identify common pathologies using unsupervised machine learning methods. We illustrate this idea by training a classifier that exploits K-means clustering to distinguish between Boson Samplers that use indistinguishable photons from those that do not. We train the model on numerical simulations of small-scale Boson Samplers and then validate the pattern recognition technique on larger numerical simulations as well as on photonic chips in both traditional Boson Sampling and scattershot experiments. The effectiveness of such method relies on particle-type-dependent internal correlations present in the output distributions. This approach performs substantially better on the test data than previous methods and underscores the ability to further generalize its operation beyond the scope of the examples that it was trained on.
|
http://arxiv.org/abs/1712.06863v2
|
[
"Iris Agresti",
"Niko Viggianiello",
"Fulvio Flamini",
"Nicolò Spagnolo",
"Andrea Crespi",
"Roberto Osellame",
"Nathan Wiebe",
"Fabio Sciarrino"
] |
2020-06-06T09:50:16Z
|
2017-12-19T10:53:18Z
|
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