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2005.09683 | Neural Collaborative Filtering vs. Matrix Factorization Revisited | Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to combine two or more embeddings, e.g., most notably in matrix factorization. In recent years, it was suggested to replace the dot product with a learned similarity e.g. using a multilayer perceptron (MLP). This approach is often referred to as neural collaborative filtering (NCF). In this work, we revisit the experiments of the NCF paper that popularized learned similarities using MLPs. First, we show that with a proper hyperparameter selection, a simple dot product substantially outperforms the proposed learned similarities. Second, while a MLP can in theory approximate any function, we show that it is non-trivial to learn a dot product with an MLP. Finally, we discuss practical issues that arise when applying MLP based similarities and show that MLPs are too costly to use for item recommendation in production environments while dot products allow to apply very efficient retrieval algorithms. We conclude that MLPs should be used with care as embedding combiner and that dot products might be a better default choice. | http://arxiv.org/pdf/2005.09683v2 | [
"Steffen Rendle",
"Walid Krichene",
"Li Zhang",
"John Anderson"
] | 2020-06-01T23:21:33Z | 2020-05-19T18:07:08Z |
2006.01322 | Saber Pro success prediction model using decision tree based learning | The primary objective of this report is to determine what influences the success rates of students who have studied in Colombia, analyzing the Saber 11, the test done at the last school year, some socioeconomic aspects and comparing the Saber Pro results with the national average. The problem this faces is to find what influences success, but it also provides an insight in the countries education dynamics and predicts one's opportunities to be prosperous. The opposite situation to the one presented in this paper could be the desertion levels, in the sense that by detecting what makes someone outstanding, these factors can say what makes one unsuccessful. The solution proposed to solve this problem was to implement a CART decision tree algorithm that helps to predict the probability that a student has of scoring higher than the mean value, based on different socioeconomic and academic factors, such as the profession of the parents of the subject parents and the results obtained on Saber 11. It was discovered that one of the most influential factors is the score in the Saber 11, on the topic of Social Studies, and that the gender of the subject is not as influential as it is usually portrayed as. The algorithm designed provided significant insight into which factors most affect the probability of success of any given person and if further pursued could be used in many given situations such as deciding which subject in school should be given more intensity to and academic curriculum in general. | http://arxiv.org/pdf/2006.01322v1 | [
"Gregorio Perez Bernal",
"Luisa Toro Villegas",
"Mauricio Toro"
] | 2020-06-02T00:19:02Z | 2020-06-02T00:19:02Z |
2006.01332 | Real-time Earthquake Early Warning with Deep Learning: Application to
the 2016 Central Apennines, Italy Earthquake Sequence | Earthquake early warning systems are required to report earthquake locations and magnitudes as quickly as possible before the damaging S wave arrival to mitigate seismic hazards. Deep learning techniques provide potential for extracting earthquake source information from full seismic waveforms instead of seismic phase picks. We developed a novel deep learning earthquake early warning system that utilizes fully convolutional networks to simultaneously detect earthquakes and estimate their source parameters from continuous seismic waveform streams. The system determines earthquake location and magnitude as soon as one station receives earthquake signals and evolutionarily improves the solutions by receiving continuous data. We apply the system to the 2016 Mw 6.0 earthquake in Central Apennines, Italy and its subsequent sequence. Earthquake locations and magnitudes can be reliably determined as early as four seconds after the earliest P phase, with mean error ranges of 6.8-3.7 km and 0.31-0.23, respectively. | http://arxiv.org/abs/2006.01332v1 | [
"Xiong Zhang",
"Miao Zhang",
"Xiao Tian"
] | 2020-06-02T01:27:25Z | 2020-06-02T01:27:25Z |
1908.08649 | Adversary-resilient Distributed and Decentralized Statistical Inference
and Machine Learning: An Overview of Recent Advances Under the Byzantine
Threat Model | While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework. In recent years, however, cybersecurity threats from malicious non-state actors and rogue entities have forced practitioners and researchers to rethink the robustness of distributed and decentralized algorithms against adversarial attacks. As a result, we now have a plethora of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models. Driven in part by the world's growing appetite for data-driven decision making, however, securing of distributed/decentralized frameworks for inference and learning against adversarial threats remains a rapidly evolving research area. In this article, we provide an overview of some of the most recent developments in this area under the threat model of Byzantine attacks. | http://arxiv.org/abs/1908.08649v3 | [
"Zhixiong Yang",
"Arpita Gang",
"Waheed U. Bajwa"
] | 2020-06-02T02:21:10Z | 2019-08-23T03:23:49Z |
2006.13305 | Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative
Adversarial Network | Generative adversarial network (GAN) has been shown to be useful in various applications, such as image recognition, text processing and scientific computing, due its strong ability to learn complex data distributions. In this study, a theory-guided generative adversarial network (TgGAN) is proposed to solve dynamic partial differential equations (PDEs). Different from standard GANs, the training term is no longer the true data and the generated data, but rather their residuals. In addition, such theories as governing equations, other physical constraints and engineering controls, are encoded into the loss function of the generator to ensure that the prediction does not only honor the training data, but also obey these theories. TgGAN is proposed for dynamic subsurface flow with heterogeneous model parameters, and the data at each time step are treated as a two-dimensional image. In this study, several numerical cases are introduced to test the performance of the TgGAN. Predicting the future response, label-free learning and learning from noisy data can be realized easily by the TgGAN model. The effects of the number of training data and the collocation points are also discussed. In order to improve the efficiency of TgGAN, the transfer learning algorithm is also employed. Numerical results demonstrate that the TgGAN model is robust and reliable for deep learning of dynamic PDEs. | http://arxiv.org/abs/2006.13305v1 | [
"Tianhao He",
"Dongxiao Zhang"
] | 2020-06-02T02:53:26Z | 2020-06-02T02:53:26Z |
2006.00778 | Variational Bayesian Inference for Crowdsourcing Predictions | Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets. In most early settings of crowdsourcing, the task involved classification, that is assigning one of a discrete set of labels to each task. Recently, however, more complex tasks have been attempted including asking crowdsource workers to assign continuous labels, or predictions. In essence, this involves the use of crowdsourcing for function estimation. We are motivated by this problem to drive applications such as collaborative prediction, that is, harnessing the wisdom of the crowd to predict quantities more accurately. To do so, we propose a Bayesian approach aimed specifically at alleviating overfitting, a typical impediment to accurate prediction models in practice. In particular, we develop a variational Bayesian technique for two different worker noise models - one that assumes workers' noises are independent and the other that assumes workers' noises have a latent low-rank structure. Our evaluations on synthetic and real-world datasets demonstrate that these Bayesian approaches perform significantly better than existing non-Bayesian approaches and are thus potentially useful for this class of crowdsourcing problems. | http://arxiv.org/pdf/2006.00778v2 | [
"Desmond Cai",
"Duc Thien Nguyen",
"Shiau Hong Lim",
"Laura Wynter"
] | 2020-06-02T02:53:30Z | 2020-06-01T08:11:50Z |
2006.00223 | Self-adaptive Re-weighted Adversarial Domain Adaptation | Existing adversarial domain adaptation methods mainly consider the marginal distribution and these methods may lead to either under transfer or negative transfer. To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution. In order to promote positive transfer and combat negative transfer, we reduce the weight of the adversarial loss for aligned features while increasing the adversarial force for those poorly aligned measured by the conditional entropy. Additionally, triplet loss leveraging source samples and pseudo-labeled target samples is employed on the confusing domain. Such metric loss ensures the distance of the intra-class sample pairs closer than the inter-class pairs to achieve the class-level alignment. In this way, the high accurate pseudolabeled target samples and semantic alignment can be captured simultaneously in the co-training process. Our method achieved low joint error of the ideal source and target hypothesis. The expected target error can then be upper bounded following Ben-David's theorem. Empirical evidence demonstrates that the proposed model outperforms state of the arts on standard domain adaptation datasets. | http://arxiv.org/pdf/2006.00223v2 | [
"Shanshan Wang",
"Lei Zhang"
] | 2020-06-02T03:08:41Z | 2020-05-30T08:35:18Z |
1907.11090 | Info Intervention | Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactual variables. This paper introduces a new info intervention to tackle these two problems, and provides causal diagrams for communication and theoretical focus based on this info intervention. Our info intervention intervenes the input/output information of causal mechanisms, while the do intervention intervenes the causal mechanisms. Consequently, the causality is viewed as information transfer in the info intervention framework. As an extension, the generalized info intervention is also proposed and studied in this paper. | http://arxiv.org/pdf/1907.11090v6 | [
"Gong Heyang",
"Zhu Ke"
] | 2020-06-02T03:25:10Z | 2019-07-24T07:31:14Z |
2006.00602 | Estimating Principal Components under Adversarial Perturbations | Robustness is a key requirement for widespread deployment of machine learning algorithms, and has received much attention in both statistics and computer science. We study a natural model of robustness for high-dimensional statistical estimation problems that we call the adversarial perturbation model. An adversary can perturb every sample arbitrarily up to a specified magnitude $delta$ measured in some $ell_q$ norm, say $ell_infty$. Our model is motivated by emerging paradigms such as low precision machine learning and adversarial training. We study the classical problem of estimating the top-$r$ principal subspace of the Gaussian covariance matrix in high dimensions, under the adversarial perturbation model. We design a computationally efficient algorithm that given corrupted data, recovers an estimate of the top-$r$ principal subspace with error that depends on a robustness parameter $kappa$ that we identify. This parameter corresponds to the $q to 2$ operator norm of the projector onto the principal subspace, and generalizes well-studied analytic notions of sparsity. Additionally, in the absence of corruptions, our algorithmic guarantees recover existing bounds for problems such as sparse PCA and its higher rank analogs. We also prove that the above dependence on the parameter $kappa$ is almost optimal asymptotically, not just in a minimax sense, but remarkably for every instance of the problem. This instance-optimal guarantee shows that the $q to 2$ operator norm of the subspace essentially characterizes the estimation error under adversarial perturbations. | http://arxiv.org/pdf/2006.00602v2 | [
"Pranjal Awasthi",
"Xue Chen",
"Aravindan Vijayaraghavan"
] | 2020-06-02T03:31:04Z | 2020-05-31T20:27:19Z |
1809.00072 | RxNN: A Framework for Evaluating Deep Neural Networks on Resistive
Crossbars | Resistive crossbars designed with non-volatile memory devices have emerged as promising building blocks for Deep Neural Network (DNN) hardware, due to their ability to compactly and efficiently realize vector-matrix multiplication (VMM), the dominant computational kernel in DNNs. However, a key challenge with resistive crossbars is that they suffer from a range of device and circuit level non-idealities such as interconnect parasitics, peripheral circuits, sneak paths, and process variations. These non-idealities can lead to errors in VMMs, eventually degrading the DNN's accuracy. It is therefore critical to study the impact of crossbar non-idealities on the accuracy of large-scale DNNs. However, this is challenging because existing device and circuit models are too slow to use in application-level evaluations. We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems. RxNN splits and maps the computations involved in each DNN layer into crossbar operations, and evaluates them using a Fast Crossbar Model (FCM) that accurately captures the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation. FCM models a crossbar-based VMM operation using three stages - non-linear models for the input and output peripheral circuits (DACs and ADCs), and an equivalent non-ideal conductance matrix for the core crossbar array. We implement RxNN by extending the Caffe machine learning framework and use it to evaluate a suite of six large-scale DNNs developed for the ImageNet Challenge. Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6%-32%) for these large-scale DNNs. To the best of our knowledge, this work is the first quantitative evaluation of the accuracy of large-scale DNNs on resistive crossbar based hardware. | http://arxiv.org/pdf/1809.00072v3 | [
"Shubham Jain",
"Abhronil Sengupta",
"Kaushik Roy",
"Anand Raghunathan"
] | 2020-06-02T03:33:11Z | 2018-08-31T22:22:53Z |
1903.11202 | Kernel based regression with robust loss function via iteratively
reweighted least squares | Least squares kernel based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called $ell_s$-loss, is proposed in this paper. With the support of novel loss function, two kernel based regressors are constructed by replacing the $ell_2$-loss in LS-SVR and ELM with the proposed $ell_s$-loss for better noise robustness. Important properties of $ell_s$-loss, including robustness, asymmetry and asymptotic approximation behaviors, are verified theoretically. Moreover, iteratively reweighted least squares (IRLS) is utilized to optimize and interpret the proposed methods from a weighted viewpoint. The convergence of the proposal are proved, and detailed analyses of robustness are given. Experiments on both artificial and benchmark datasets confirm the validity of the proposed methods. | http://arxiv.org/pdf/1903.11202v2 | [
"Hongwei Dong",
"Liming Yang"
] | 2020-06-02T03:57:26Z | 2019-03-27T00:40:18Z |
1703.09068 | Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels | Hawkes Processes capture self-excitation and mutual-excitation between events when the arrival of an event makes future events more likely to happen. Identification of such temporal covariance can reveal the underlying structure to better predict future events. In this paper, we present a new framework to decompose discrete events with a composition of multiple self-triggering kernels. The composition scheme allows us to decompose empirical covariance densities into the sum or the product of base kernels which are easily interpretable. Here, we present the first multiplicative kernel composition methods for Hawkes Processes. We demonstrate that the new automatic kernel decomposition procedure outperforms the existing methods on the prediction of discrete events in real-world data. | http://arxiv.org/pdf/1703.09068v6 | [
"Rafael Lima",
"Jaesik Choi"
] | 2020-06-02T04:05:46Z | 2017-03-27T15:25:54Z |
2006.01381 | Identifying Fake Profiles in LinkedIn | As organizations increasingly rely on professionally oriented networks such as LinkedIn (the largest such social network) for building business connections, there is increasing value in having one's profile noticed within the network. As this value increases, so does the temptation to misuse the network for unethical purposes. Fake profiles have an adverse effect on the trustworthiness of the network as a whole, and can represent significant costs in time and effort in building a connection based on fake information. Unfortunately, fake profiles are difficult to identify. Approaches have been proposed for some social networks; however, these generally rely on data that are not publicly available for LinkedIn profiles. In this research, we identify the minimal set of profile data necessary for identifying fake profiles in LinkedIn, and propose an appropriate data mining approach for fake profile identification. We demonstrate that, even with limited profile data, our approach can identify fake profiles with 87% accuracy and 94% True Negative Rate, which is comparable to the results obtained based on larger data sets and more expansive profile information. Further, when compared to approaches using similar amounts and types of data, our method provides an improvement of approximately 14% accuracy. | http://arxiv.org/pdf/2006.01381v1 | [
"Shalinda Adikari",
"Kaushik Dutta"
] | 2020-06-02T04:15:20Z | 2020-06-02T04:15:20Z |
2006.01395 | Feature-weighted elastic net: using "features of features" for better
prediction | In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the feature-weighted elastic net ("fwelnet"), uses these "features of features" to adapt the relative penalties on the feature coefficients in the elastic net penalty. In our simulations, fwelnet outperforms the lasso in terms of test mean squared error and usually gives an improvement in true positive rate or false positive rate for feature selection. We also apply this method to early prediction of preeclampsia, where fwelnet outperforms the lasso in terms of 10-fold cross-validated area under the curve (0.86 vs. 0.80). We also provide a connection between fwelnet and the group lasso and suggest how fwelnet might be used for multi-task learning. | http://arxiv.org/pdf/2006.01395v1 | [
"J. Kenneth Tay",
"Nima Aghaeepour",
"Trevor Hastie",
"Robert Tibshirani"
] | 2020-06-02T05:18:15Z | 2020-06-02T05:18:15Z |
2006.01400 | Approximation Guarantees of Local Search Algorithms via Localizability
of Set Functions | This paper proposes a new framework for providing approximation guarantees of local search algorithms. Local search is a basic algorithm design technique and is widely used for various combinatorial optimization problems. To analyze local search algorithms for set function maximization, we propose a new notion called localizability of set functions, which measures how effective local improvement is. Moreover, we provide approximation guarantees of standard local search algorithms under various combinatorial constraints in terms of localizability. The main application of our framework is sparse optimization, for which we show that restricted strong concavity and restricted smoothness of the objective function imply localizability, and further develop accelerated versions of local search algorithms. We conduct experiments in sparse regression and structure learning of graphical models to confirm the practical efficiency of the proposed local search algorithms. | http://arxiv.org/pdf/2006.01400v1 | [
"Kaito Fujii"
] | 2020-06-02T05:37:52Z | 2020-06-02T05:37:52Z |
2006.01402 | A Smart Background Scheduler for Storage Systems | In today's enterprise storage systems, supported data services such as snapshot delete or drive rebuild can cause tremendous performance interference if executed inline along with heavy foreground IO, often leading to missing SLOs (Service Level Objectives). Typical storage system applications such as web or VDI (Virtual Desktop Infrastructure) follow a repetitive high/low workload pattern that can be learned and forecasted. We propose a priority-based background scheduler that learns this repetitive pattern and allows storage systems to maintain peak performance and in turn meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies, system resources are dedicated to service foreground IO requests and any background processing that can be deferred are recorded to be processed in future idle cycles as long as forecast shows that storage pool has remaining capacity. The smart background scheduler adopts a resource partitioning model that allows both foreground and background IO to execute together as long as foreground IOs are not impacted where the scheduler harness any free cycle to clear background debt. Using traces from VDI application, we show how our technique surpasses a method that statically limit the deferred background debt and improve SLO violations from 54.6% when using a fixed background debt watermark to merely a 6.2% if dynamically set by our smart background scheduler. | http://arxiv.org/pdf/2006.01402v1 | [
"Maher Kachmar",
"David Kaeli"
] | 2020-06-02T05:39:56Z | 2020-06-02T05:39:56Z |
2006.01408 | Exploring the role of Input and Output Layers of a Deep Neural Network
in Adversarial Defense | Deep neural networks are learning models having achieved state of the art performance in many fields like prediction, computer vision, language processing and so on. However, it has been shown that certain inputs exist which would not trick a human normally, but may mislead the model completely. These inputs are known as adversarial inputs. These inputs pose a high security threat when such models are used in real world applications. In this work, we have analyzed the resistance of three different classes of fully connected dense networks against the rarely tested non-gradient based adversarial attacks. These classes are created by manipulating the input and output layers. We have proven empirically that owing to certain characteristics of the network, they provide a high robustness against these attacks, and can be used in fine tuning other models to increase defense against adversarial attacks. | http://arxiv.org/pdf/2006.01408v1 | [
"Jay N. Paranjape",
"Rahul Kumar Dubey",
"Vijendran V Gopalan"
] | 2020-06-02T06:15:46Z | 2020-06-02T06:15:46Z |
2004.02353 | Adaptive Explainable Neural Networks (AxNNs) | While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models (through explainable neural networks) using a two-stage process. This can be done using either a boosting or a stacking ensemble. For interpretability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects. The computations are inherited from Google's open source tool AdaNet and can be efficiently accelerated by training with distributed computing. The results are illustrated on simulated and real datasets. | http://arxiv.org/pdf/2004.02353v2 | [
"Jie Chen",
"Joel Vaughan",
"Vijayan N. Nair",
"Agus Sudjianto"
] | 2020-06-02T06:18:04Z | 2020-04-05T23:40:57Z |
2006.01424 | Image Super-Resolution with Cross-Scale Non-Local Attention and
Exhaustive Self-Exemplars Mining | Deep convolution-based single image super-resolution (SISR) networks embrace the benefits of learning from large-scale external image resources for local recovery, yet most existing works have ignored the long-range feature-wise similarities in natural images. Some recent works have successfully leveraged this intrinsic feature correlation by exploring non-local attention modules. However, none of the current deep models have studied another inherent property of images: cross-scale feature correlation. In this paper, we propose the first Cross-Scale Non-Local (CS-NL) attention module with integration into a recurrent neural network. By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution (LR) image. The performance of SISR is significantly improved by exhaustively integrating all possible priors. Extensive experiments demonstrate the effectiveness of the proposed CS-NL module by setting new state-of-the-arts on multiple SISR benchmarks. | http://arxiv.org/pdf/2006.01424v1 | [
"Yiqun Mei",
"Yuchen Fan",
"Yuqian Zhou",
"Lichao Huang",
"Thomas S. Huang",
"Humphrey Shi"
] | 2020-06-02T07:08:58Z | 2020-06-02T07:08:58Z |
2004.11637 | Sparse Array Selection Across Arbitrary Sensor Geometries with Deep
Transfer Learning | Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated labeled training data are unavailable and, more so, for arbitrary array configurations. To address this, we adopt a deep transfer learning (TL) approach, wherein we train a deep convolutional neural network (CNN) with data of a source sensor array for which calibrated data are readily available and reuse this pre-trained CNN for a different, data-insufficient target array geometry to perform sparse array selection. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL-CNN on the target model than the CNN trained with insufficient data from the same model. In particular, our TL framework provides approximately 20% higher sensor selection accuracy and 10% improvement in the direction-of-arrival estimation error. | http://arxiv.org/abs/2004.11637v2 | [
"Ahmet M. Elbir",
"Kumar Vijay Mishra"
] | 2020-06-02T07:40:35Z | 2020-04-24T10:10:48Z |
2006.01436 | Modified Hard Thresholding Pursuit with Regularization Assisted Support
Identification | Hard thresholding pursuit (HTP) is a recently proposed iterative sparse recovery algorithm which is a result of combination of a support selection step from iterated hard thresholding (IHT) and an estimation step from the orthogonal matching pursuit (OMP). HTP has been seen to enjoy improved recovery guarantee along with enhanced speed of convergence. Much of the success of HTP can be attributed to its improved support selection capability due to the support selection step from IHT. In this paper, we propose a generalized HTP algorithm, called regularized HTP (RHTP), where the support selection step of HTP is replaced by a IHT-type support selection where the cost function is replaced by a regularized cost function, while the estimation step continues to use the least squares function. With decomposable regularizer, satisfying certain regularity conditions, the RHTP algorithm is shown to produce a sequence dynamically equivalent to a sequence evolving according to a HTP-like evolution, where the identification stage has a gradient premultiplied with a time-varying diagonal matrix. RHTP is also proven, both theoretically, and numerically, to enjoy faster convergence vis-a-vis HTP with both noiseless and noisy measurement vectors. | http://arxiv.org/pdf/2006.01436v1 | [
"Samrat Mukhopadhyay",
"Mrityunjoy Chakraborty"
] | 2020-06-02T07:54:54Z | 2020-06-02T07:54:54Z |
2006.01446 | Identification of hydrodynamic instability by convolutional neural
networks | The onset of hydrodynamic instabilities is of great importance in both industry and daily life, due to the dramatic mechanical and thermodynamic changes for different types of flow motions. In this paper, modern machine learning techniques, especially the convolutional neural networks (CNN), are applied to identify the transition between different flow motions raised by hydrodynamic instability, as well as critical non-dimensionalized parameters for characterizing this transit. CNN not only correctly predicts the critical transition values for both Taylor-Couette (TC) flow and Rayleigh- B'enard (RB) convection under various setups and conditions, but also shows an outstanding performance on robustness and noise-tolerance. In addition, key spatial features used for classifying different flow patterns are revealed by the principal component analysis. | http://arxiv.org/pdf/2006.01446v1 | [
"Wuyue Yang",
"Liangrong Peng",
"Yi Zhu",
"Liu Hong"
] | 2020-06-02T08:32:08Z | 2020-06-02T08:32:08Z |
1909.03790 | Graph Random Neural Features for Distance-Preserving Graph
Representations | We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding naturally deals with graph isomorphism and preserves the metric structure of the graph domain, in probability. In addition to being an explicit embedding method, it also allows us to efficiently and effectively approximate graph metric distances (as well as complete kernel functions); a criterion to select the embedding dimension trading off the approximation accuracy with the computational cost is also provided. GRNF can be used within traditional processing methods or as a training-free input layer of a graph neural network. The theoretical guarantees that accompany GRNF ensure that the considered graph distance is metric, hence allowing to distinguish any pair of non-isomorphic graphs. | http://arxiv.org/pdf/1909.03790v3 | [
"Daniele Zambon",
"Cesare Alippi",
"Lorenzo Livi"
] | 2020-06-02T08:37:17Z | 2019-09-09T12:13:46Z |
2006.01451 | Careful analysis of XRD patterns with Attention | The important peaks related to the physical properties of a lithium ion rechargeable battery were extracted from the measured X ray diffraction spectrum by a convolutional neural network based on the Attention mechanism. Among the deep features, the lattice constant of the cathodic active material was selected as a cell voltage predictor, and the crystallographic behavior of the active anodic and cathodic materials revealed the rate property during the charge discharge states. The machine learning automatically selected the significant peaks from the experimental spectrum. Applying the Attention mechanism with appropriate objective variables in multi task trained models, one can selectively visualize the correlations between interesting physical properties. As the deep features are automatically defined, this approach can adapt to the conditions of various physical experiments. | http://arxiv.org/pdf/2006.01451v1 | [
"Koichi Kano",
"Takashi Segi",
"Hiroshi Ozono"
] | 2020-06-02T08:44:05Z | 2020-06-02T08:44:05Z |
2006.01456 | Perturbation Analysis of Gradient-based Adversarial Attacks | After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully crafted samples. Although empirical results on the effectiveness of adversarial example generation methods against defense mechanisms are discussed in detail in the literature, an in-depth study of the theoretical properties and the perturbation effectiveness of these adversarial attacks has largely been lacking. In this paper, we investigate the objective functions of three popular methods for adversarial example generation: the L-BFGS attack, the Iterative Fast Gradient Sign attack, and Carlini & Wagner's attack (CW). Specifically, we perform a comparative and formal analysis of the loss functions underlying the aforementioned attacks while laying out large-scale experimental results on ImageNet dataset. This analysis exposes (1) the faster optimization speed as well as the constrained optimization space of the cross-entropy loss, (2) the detrimental effects of using the signature of the cross-entropy loss on optimization precision as well as optimization space, and (3) the slow optimization speed of the logit loss in the context of adversariality. Our experiments reveal that the Iterative Fast Gradient Sign attack, which is thought to be fast for generating adversarial examples, is the worst attack in terms of the number of iterations required to create adversarial examples in the setting of equal perturbation. Moreover, our experiments show that the underlying loss function of CW, which is criticized for being substantially slower than other adversarial attacks, is not that much slower than other loss functions. Finally, we analyze how well neural networks can identify adversarial perturbations generated by the attacks under consideration, hereby revisiting the idea of adversarial retraining on ImageNet. | http://arxiv.org/abs/2006.01456v1 | [
"Utku Ozbulak",
"Manvel Gasparyan",
"Wesley De Neve",
"Arnout Van Messem"
] | 2020-06-02T08:51:37Z | 2020-06-02T08:51:37Z |
1905.03853 | Genuinely Distributed Byzantine Machine Learning | Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a Byzantine behavior. Several approaches have been proposed recently to tolerate Byzantine workers. Yet all require trusting a central parameter server. We initiate in this paper the study of the ``general'' Byzantine-resilient distributed machine learning problem where no individual component is trusted. We show that this problem can be solved in an asynchronous system, despite the presence of $frac{1}{3}$ Byzantine parameter servers and $frac{1}{3}$ Byzantine workers (which is optimal). We present a new algorithm, ByzSGD, which solves the general Byzantine-resilient distributed machine learning problem by relying on three major schemes. The first, Scatter/Gather, is a communication scheme whose goal is to bound the maximum drift among models on correct servers. The second, Distributed Median Contraction (DMC), leverages the geometric properties of the median in high dimensional spaces to bring parameters within the correct servers back close to each other, ensuring learning convergence. The third, Minimum-Diameter Averaging (MDA), is a statistically-robust gradient aggregation rule whose goal is to tolerate Byzantine workers. MDA requires loose bound on the variance of non-Byzantine gradient estimates, compared to existing alternatives (e.g., Krum). Interestingly, ByzSGD ensures Byzantine resilience without adding communication rounds (on a normal path), compared to vanilla non-Byzantine alternatives. ByzSGD requires, however, a larger number of messages which, we show, can be reduced if we assume synchrony. | http://arxiv.org/pdf/1905.03853v2 | [
"El-Mahdi El-Mhamdi",
"Rachid Guerraoui",
"Arsany Guirguis",
"Lê Nguyên Hoang",
"Sébastien Rouault"
] | 2020-06-02T08:57:00Z | 2019-05-05T16:14:30Z |
2006.04520 | Maximizing Cumulative User Engagement in Sequential Recommendation: An
Online Optimization Perspective | To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging user browsing (i.e., more items exposured). Existing works often study these two tasks separately, thus tend to result in sub-optimal results. In this paper, we study this problem from an online optimization perspective, and propose a flexible and practical framework to explicitly tradeoff longer user browsing length and high immediate user engagement. Specifically, by considering items as actions, user's requests as states and user leaving as an absorbing state, we formulate each user's behavior as a personalized Markov decision process (MDP), and the problem of maximizing cumulative user engagement is reduced to a stochastic shortest path (SSP) problem. Meanwhile, with immediate user engagement and quit probability estimation, it is shown that the SSP problem can be efficiently solved via dynamic programming. Experiments on real-world datasets demonstrate the effectiveness of the proposed approach. Moreover, this approach is deployed at a large E-commerce platform, achieved over 7% improvement of cumulative clicks. | http://arxiv.org/pdf/2006.04520v1 | [
"Yifei Zhao",
"Yu-Hang Zhou",
"Mingdong Ou",
"Huan Xu",
"Nan Li"
] | 2020-06-02T09:02:51Z | 2020-06-02T09:02:51Z |
2006.02227 | Variational Mutual Information Maximization Framework for VAE Latent
Codes with Continuous and Discrete Priors | Learning interpretable and disentangled representations of data is a key topic in machine learning research. Variational Autoencoder (VAE) is a scalable method for learning directed latent variable models of complex data. It employs a clear and interpretable objective that can be easily optimized. However, this objective does not provide an explicit measure for the quality of latent variable representations which may result in their poor quality. We propose Variational Mutual Information Maximization Framework for VAE to address this issue. In comparison to other methods, it provides an explicit objective that maximizes lower bound on mutual information between latent codes and observations. The objective acts as a regularizer that forces VAE to not ignore the latent variable and allows one to select particular components of it to be most informative with respect to the observations. On top of that, the proposed framework provides a way to evaluate mutual information between latent codes and observations for a fixed VAE model. We have conducted our experiments on VAE models with Gaussian and joint Gaussian and discrete latent variables. Our results illustrate that the proposed approach strengthens relationships between latent codes and observations and improves learned representations. | http://arxiv.org/abs/2006.02227v1 | [
"Andriy Serdega",
"Dae-Shik Kim"
] | 2020-06-02T09:05:51Z | 2020-06-02T09:05:51Z |
2006.01488 | Meta Learning as Bayes Risk Minimization | Meta-Learning is a family of methods that use a set of interrelated tasks to learn a model that can quickly learn a new query task from a possibly small contextual dataset. In this study, we use a probabilistic framework to formalize what it means for two tasks to be related and reframe the meta-learning problem into the problem of Bayesian risk minimization (BRM). In our formulation, the BRM optimal solution is given by the predictive distribution computed from the posterior distribution of the task-specific latent variable conditioned on the contextual dataset, and this justifies the philosophy of Neural Process. However, the posterior distribution in Neural Process violates the way the posterior distribution changes with the contextual dataset. To address this problem, we present a novel Gaussian approximation for the posterior distribution that generalizes the posterior of the linear Gaussian model. Unlike that of the Neural Process, our approximation of the posterior distributions converges to the maximum likelihood estimate with the same rate as the true posterior distribution. We also demonstrate the competitiveness of our approach on benchmark datasets. | http://arxiv.org/pdf/2006.01488v1 | [
"Shin-ichi Maeda",
"Toshiki Nakanishi",
"Masanori Koyama"
] | 2020-06-02T09:38:00Z | 2020-06-02T09:38:00Z |
2005.03585 | Domain Adaptation in Highly Imbalanced and Overlapping Datasets | In many machine learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised domain adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from electronic health records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection are discussed. | http://arxiv.org/pdf/2005.03585v2 | [
"Ran Ilan Ber",
"Tom Haramaty"
] | 2020-06-02T10:23:16Z | 2020-05-07T16:15:45Z |
2006.02226 | Deep Receiver Design for Multi-carrier Waveforms Using CNNs | In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems. In particular, we propose to use a convolutional neural network (CNN) for jointly detection and demodulation of the received signal at the receiver in wireless environments. We compare our proposed architecture to the classical methods and demonstrate that our proposed CNN-based architecture can perform better on different multi-carrier forms including OFDM and GFDM in various simulations. Furthermore, we compare the total number of required parameters for each network for memory requirements. | http://arxiv.org/pdf/2006.02226v1 | [
"Yasin Yildirim",
"Sedat Ozer",
"Hakan Ali Cirpan"
] | 2020-06-02T10:29:05Z | 2020-06-02T10:29:05Z |
2006.01510 | Recht-Ré Noncommutative Arithmetic-Geometric Mean Conjecture is False | Stochastic optimization algorithms have become indispensable in modern machine learning. An unresolved foundational question in this area is the difference between with-replacement sampling and without-replacement sampling -- does the latter have superior convergence rate compared to the former? A groundbreaking result of Recht and R'e reduces the problem to a noncommutative analogue of the arithmetic-geometric mean inequality where $n$ positive numbers are replaced by $n$ positive definite matrices. If this inequality holds for all $n$, then without-replacement sampling indeed outperforms with-replacement sampling. The conjectured Recht-R'e inequality has so far only been established for $n = 2$ and a special case of $n = 3$. We will show that the Recht-R'e conjecture is false for general $n$. Our approach relies on the noncommutative Positivstellensatz, which allows us to reduce the conjectured inequality to a semidefinite program and the validity of the conjecture to certain bounds for the optimum values, which we show are false as soon as $n = 5$. | http://arxiv.org/pdf/2006.01510v1 | [
"Zehua Lai",
"Lek-Heng Lim"
] | 2020-06-02T10:34:04Z | 2020-06-02T10:34:04Z |
2006.01538 | WikiBERT models: deep transfer learning for many languages | Deep neural language models such as BERT have enabled substantial recent advances in many natural language processing tasks. Due to the effort and computational cost involved in their pre-training, language-specific models are typically introduced only for a small number of high-resource languages such as English. While multilingual models covering large numbers of languages are available, recent work suggests monolingual training can produce better models, and our understanding of the tradeoffs between mono- and multilingual training is incomplete. In this paper, we introduce a simple, fully automated pipeline for creating language-specific BERT models from Wikipedia data and introduce 42 new such models, most for languages up to now lacking dedicated deep neural language models. We assess the merits of these models using the state-of-the-art UDify parser on Universal Dependencies data, contrasting performance with results using the multilingual BERT model. We find that UDify using WikiBERT models outperforms the parser using mBERT on average, with the language-specific models showing substantially improved performance for some languages, yet limited improvement or a decrease in performance for others. We also present preliminary results as first steps toward an understanding of the conditions under which language-specific models are most beneficial. All of the methods and models introduced in this work are available under open licenses from https://github.com/turkunlp/wikibert. | http://arxiv.org/pdf/2006.01538v1 | [
"Sampo Pyysalo",
"Jenna Kanerva",
"Antti Virtanen",
"Filip Ginter"
] | 2020-06-02T11:57:53Z | 2020-06-02T11:57:53Z |
1910.13321 | Semantic Object Accuracy for Generative Text-to-Image Synthesis | Generative adversarial networks conditioned on textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate images based on complex image captions from a heterogeneous domain. Furthermore, quantitatively evaluating these text-to-image models is challenging, as most evaluation metrics only judge image quality but not the conformity between the image and its caption. To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption. The SOA uses a pre-trained object detector to evaluate if a generated image contains objects that are mentioned in the image caption, e.g. whether an image generated from "a car driving down the street" contains a car. We perform a user study comparing several text-to-image models and show that our SOA metric ranks the models the same way as humans, whereas other metrics such as the Inception Score do not. Our evaluation also shows that models which explicitly model objects outperform models which only model global image characteristics. | http://arxiv.org/abs/1910.13321v2 | [
"Tobias Hinz",
"Stefan Heinrich",
"Stefan Wermter"
] | 2020-06-02T12:25:16Z | 2019-10-29T15:35:52Z |
2005.09910 | Multitask Learning with Single Gradient Step Update for Task Balancing | Multitask learning is a methodology to boost generalization performance and also reduce computational intensity and memory usage. However, learning multiple tasks simultaneously can be more difficult than learning a single task because it can cause imbalance among tasks. To address the imbalance problem, we propose an algorithm to balance between tasks at the gradient level by applying gradient-based meta-learning to multitask learning. The proposed method trains shared layers and task-specific layers separately so that the two layers with different roles in a multitask network can be fitted to their own purposes. In particular, the shared layer that contains informative knowledge shared among tasks is trained by employing single gradient step update and inner/outer loop training to mitigate the imbalance problem at the gradient level. We apply the proposed method to various multitask computer vision problems and achieve state-of-the-art performance. | http://arxiv.org/pdf/2005.09910v2 | [
"Sungjae Lee",
"Youngdoo Son"
] | 2020-06-02T12:29:42Z | 2020-05-20T08:34:20Z |
2006.01561 | Studying The Effect of MIL Pooling Filters on MIL Tasks | There are different multiple instance learning (MIL) pooling filters used in MIL models. In this paper, we study the effect of different MIL pooling filters on the performance of MIL models in real world MIL tasks. We designed a neural network based MIL framework with 5 different MIL pooling filters: `max', `mean', `attention', `distribution' and `distribution with attention'. We also formulated 5 different MIL tasks on a real world lymph node metastases dataset. We found that the performance of our framework in a task is different for different filters. We also observed that the performances of the five pooling filters are also different from task to task. Hence, the selection of a correct MIL pooling filter for each MIL task is crucial for better performance. Furthermore, we noticed that models with `distribution' and `distribution with attention' pooling filters consistently perform well in almost all of the tasks. We attribute this phenomena to the amount of information captured by `distribution' based pooling filters. While point estimate based pooling filters, like `max' and `mean', produce point estimates of distributions, `distribution' based pooling filters capture the full information in distributions. Lastly, we compared the performance of our neural network model with `distribution' pooling filter with the performance of the best MIL methods in the literature on classical MIL datasets and our model outperformed the others. | http://arxiv.org/pdf/2006.01561v1 | [
"Mustafa Umit Oner",
"Jared Marc Song Kye-Jet",
"Hwee Kuan Lee",
"Wing-Kin Sung"
] | 2020-06-02T12:33:03Z | 2020-06-02T12:33:03Z |
2006.01570 | CNNs on Surfaces using Rotation-Equivariant Features | This paper is concerned with a fundamental problem in geometric deep learning that arises in the construction of convolutional neural networks on surfaces. Due to curvature, the transport of filter kernels on surfaces results in a rotational ambiguity, which prevents a uniform alignment of these kernels on the surface. We propose a network architecture for surfaces that consists of vector-valued, rotation-equivariant features. The equivariance property makes it possible to locally align features, which were computed in arbitrary coordinate systems, when aggregating features in a convolution layer. The resulting network is agnostic to the choices of coordinate systems for the tangent spaces on the surface. We implement our approach for triangle meshes. Based on circular harmonic functions, we introduce convolution filters for meshes that are rotation-equivariant at the discrete level. We evaluate the resulting networks on shape correspondence and shape classifications tasks and compare their performance to other approaches. | http://arxiv.org/abs/2006.01570v1 | [
"Ruben Wiersma",
"Elmar Eisemann",
"Klaus Hildebrandt"
] | 2020-06-02T12:46:00Z | 2020-06-02T12:46:00Z |
2006.01606 | An Informal Introduction to Multiplet Neural Networks | In the artificial neuron, I replace the dot product with the weighted Lehmer mean, which may emulate different cases of a generalized mean. The single neuron instance is replaced by a multiplet of neurons which have the same averaging weights. A group of outputs feed forward, in lieu of the single scalar. The generalization parameter is typically set to a different value for each neuron in the multiplet. I further extend the concept to a multiplet taken from the Gini mean. Derivatives with respect to the weight parameters and with respect to the two generalization parameters are given. Some properties of the network are investigated, showing the capacity to emulate the classical exclusive-or problem organically in two layers and perform some multiplication and division. The network can instantiate truncated power series and variants, which can be used to approximate different functions, provided that parameters are constrained. Moreover, a mean case slope score is derived that can facilitate a learning-rate novelty based on homogeneity of the selected elements. The multiplet neuron equation provides a way to segment regularization timeframes and approaches. | http://arxiv.org/pdf/2006.01606v1 | [
"Nathan E. Frick"
] | 2020-06-02T13:46:32Z | 2020-06-02T13:46:32Z |
2006.01610 | Combining Reinforcement Learning and Constraint Programming for
Combinatorial Optimization | Combinatorial optimization has found applications in numerous fields, from aerospace to transportation planning and economics. The goal is to find an optimal solution among a finite set of possibilities. The well-known challenge one faces with combinatorial optimization is the state-space explosion problem: the number of possibilities grows exponentially with the problem size, which makes solving intractable for large problems. In the last years, deep reinforcement learning (DRL) has shown its promise for designing good heuristics dedicated to solve NP-hard combinatorial optimization problems. However, current approaches have two shortcomings: (1) they mainly focus on the standard travelling salesman problem and they cannot be easily extended to other problems, and (2) they only provide an approximate solution with no systematic ways to improve it or to prove optimality. In another context, constraint programming (CP) is a generic tool to solve combinatorial optimization problems. Based on a complete search procedure, it will always find the optimal solution if we allow an execution time large enough. A critical design choice, that makes CP non-trivial to use in practice, is the branching decision, directing how the search space is explored. In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems. The core of our approach is based on a dynamic programming formulation, that acts as a bridge between both techniques. We experimentally show that our solver is efficient to solve two challenging problems: the traveling salesman problem with time windows, and the 4-moments portfolio optimization problem. Results obtained show that the framework introduced outperforms the stand-alone RL and CP solutions, while being competitive with industrial solvers. | http://arxiv.org/pdf/2006.01610v1 | [
"Quentin Cappart",
"Thierry Moisan",
"Louis-Martin Rousseau",
"Isabeau Prémont-Schwarz",
"Andre Cire"
] | 2020-06-02T13:54:27Z | 2020-06-02T13:54:27Z |
2006.01035 | Data-Driven Prediction of Embryo Implantation Probability Using IVF
Time-lapse Imaging | The process of fertilizing a human egg outside the body in order to help those suffering from infertility to conceive is known as in vitro fertilization (IVF). Despite being the most effective method of assisted reproductive technology (ART), the average success rate of IVF is a mere 20-40%. One step that is critical to the success of the procedure is selecting which embryo to transfer to the patient, a process typically conducted manually and without any universally accepted and standardized criteria. In this paper we describe a novel data-driven system trained to directly predict embryo implantation probability from embryogenesis time-lapse imaging videos. Using retrospectively collected videos from 272 embryos, we demonstrate that, when compared to an external panel of embryologists, our algorithm results in a 12% increase of positive predictive value and a 29% increase of negative predictive value. | http://arxiv.org/pdf/2006.01035v2 | [
"David H. Silver",
"Martin Feder",
"Yael Gold-Zamir",
"Avital L. Polsky",
"Shahar Rosentraub",
"Efrat Shachor",
"Adi Weinberger",
"Pavlo Mazur",
"Valery D. Zukin",
"Alex M. Bronstein"
] | 2020-06-02T14:02:44Z | 2020-06-01T16:04:08Z |
2006.01620 | Deep neural networks for inverse problems with pseudodifferential
operators: an application to limited-angle tomography | We propose a novel convolutional neural network (CNN), called $Psi$DONet, designed for learning pseudodifferential operators ($Psi$DOs) in the context of linear inverse problems. Our starting point is the Iterative Soft Thresholding Algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling and convolution, which characterize our $Psi$DONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited angle X-ray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of $Psi$DONet on simulated data from limited-angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are $Psi$DOs or Fourier integral operators. | http://arxiv.org/pdf/2006.01620v1 | [
"Tatiana A. Bubba",
"Mathilde Galinier",
"Matti Lassas",
"Marco Prato",
"Luca Ratti",
"Samuli Siltanen"
] | 2020-06-02T14:03:41Z | 2020-06-02T14:03:41Z |
2006.13815 | Local Interpretability of Calibrated Prediction Models: A Case of Type 2
Diabetes Mellitus Screening Test | Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions reached by a ML model. Interpretability is of extremely high importance in many fields of healthcare due to high levels of risk related to decisions based on ML models. Calibration of the ML model outputs is another issue often overlooked in the application of ML models in practice. This paper represents an early work in examination of prediction model calibration impact on the interpretability of the results. We present a use case of a patient in diabetes screening prediction scenario and visualize results using three different techniques to demonstrate the differences between calibrated and uncalibrated regularized regression model. | http://arxiv.org/pdf/2006.13815v1 | [
"Simon Kocbek",
"Primoz Kocbek",
"Leona Cilar",
"Gregor Stiglic"
] | 2020-06-02T14:14:35Z | 2020-06-02T14:14:35Z |
2006.01659 | Surprisal-Triggered Conditional Computation with Neural Networks | Autoregressive neural network models have been used successfully for sequence generation, feature extraction, and hypothesis scoring. This paper presents yet another use for these models: allocating more computation to more difficult inputs. In our model, an autoregressive model is used both to extract features and to predict observations in a stream of input observations. The surprisal of the input, measured as the negative log-likelihood of the current observation according to the autoregressive model, is used as a measure of input difficulty. This in turn determines whether a small, fast network, or a big, slow network, is used. Experiments on two speech recognition tasks show that our model can match the performance of a baseline in which the big network is always used with 15% fewer FLOPs. | http://arxiv.org/pdf/2006.01659v1 | [
"Loren Lugosch",
"Derek Nowrouzezahrai",
"Brett H. Meyer"
] | 2020-06-02T14:34:24Z | 2020-06-02T14:34:24Z |
2006.01673 | Learning Opinion Dynamics From Social Traces | Opinion dynamics - the research field dealing with how people's opinions form and evolve in a social context - traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, their predictive power is limited. Moreover, parameter calibration and model selection are manual and difficult tasks. In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces. Given a set of observables (e.g., actions and interactions between agents), our model can recover the most-likely latent opinion trajectories that are compatible with the assumptions about the process dynamics. This type of model retains the benefits of agent-based ones (i.e., causal interpretation), while adding the ability to perform model selection and hypothesis testing on real data. We showcase our proposal by translating a classical agent-based model of opinion dynamics into its generative counterpart. We then design an inference algorithm based on online expectation maximization to learn the latent parameters of the model. Such algorithm can recover the latent opinion trajectories from traces generated by the classical agent-based model. In addition, it can identify the most likely set of macro parameters used to generate a data trace, thus allowing testing of sociological hypotheses. Finally, we apply our model to real-world data from Reddit to explore the long-standing question about the impact of backfire effect. Our results suggest a low prominence of the effect in Reddit's political conversation. | http://arxiv.org/abs/2006.01673v1 | [
"Corrado Monti",
"Gianmarco De Francisci Morales",
"Francesco Bonchi"
] | 2020-06-02T14:48:17Z | 2020-06-02T14:48:17Z |
2006.08702 | Application of Machine Learning to Predict the Risk of Alzheimer's
Disease: An Accurate and Practical Solution for Early Diagnostics | Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of Aging (AIBL). We systematically explore different machine learning models, pre-processing methods and feature selection techniques. The most performant model demonstrates greater than 90% accuracy and recall in predicting AD, and the results generalize across sub-studies of ADNI and to the independent AIBL study. We also demonstrate that these results are robust to reducing the number of clinical visits or tests per visit. Using a metaclassification algorithm and longitudinal data analysis we are able to produce a "lean" diagnostic protocol with only 3 tests and 4 clinical visits that can predict Alzheimer's development with 87% accuracy and 79% recall. This novel work can be adapted into a practical early diagnostic tool for predicting the development of Alzheimer's that maximizes accuracy while minimizing the number of necessary diagnostic tests and clinical visits. | http://arxiv.org/pdf/2006.08702v1 | [
"Courtney Cochrane",
"David Castineira",
"Nisreen Shiban",
"Pavlos Protopapas"
] | 2020-06-02T14:52:51Z | 2020-06-02T14:52:51Z |
2006.02904 | Construction of 'Support Vector' Machine Feature Spaces via Deformed
Weyl-Heisenberg Algebra | This paper uses deformed coherent states, based on a deformed Weyl-Heisenberg algebra that unifies the well-known SU(2), Weyl-Heisenberg, and SU(1,1) groups, through a common parameter. We show that deformed coherent states provide the theoretical foundation of a meta-kernel function, that is a kernel which in turn defines kernel functions. Kernel functions drive developments in the field of machine learning and the meta-kernel function presented in this paper opens new theoretical avenues for the definition and exploration of kernel functions. The meta-kernel function applies associated revolution surfaces as feature spaces identified with non-linear coherent states. An empirical investigation compares the deformed SU(2) and SU(1,1) kernels derived from the meta-kernel which shows performance similar to the Radial Basis kernel, and offers new insights (based on the deformed Weyl-Heisenberg algebra). | http://arxiv.org/pdf/2006.02904v1 | [
"Shahram Dehdashti",
"Catarina Moreira",
"Abdul Karim Obeid",
"Peter Bruza"
] | 2020-06-02T14:53:00Z | 2020-06-02T14:53:00Z |
2006.01683 | Channel Distillation: Channel-Wise Attention for Knowledge Distillation | Knowledge distillation is to transfer the knowledge from the data learned by the teacher network to the student network, so that the student has the advantage of less parameters and less calculations, and the accuracy is close to the teacher. In this paper, we propose a new distillation method, which contains two transfer distillation strategies and a loss decay strategy. The first transfer strategy is based on channel-wise attention, called Channel Distillation (CD). CD transfers the channel information from the teacher to the student. The second is Guided Knowledge Distillation (GKD). Unlike Knowledge Distillation (KD), which allows the student to mimic each sample's prediction distribution of the teacher, GKD only enables the student to mimic the correct output of the teacher. The last part is Early Decay Teacher (EDT). During the training process, we gradually decay the weight of the distillation loss. The purpose is to enable the student to gradually control the optimization rather than the teacher. Our proposed method is evaluated on ImageNet and CIFAR100. On ImageNet, we achieve 27.68% of top-1 error with ResNet18, which outperforms state-of-the-art methods. On CIFAR100, we achieve surprising result that the student outperforms the teacher. Code is available at https://github.com/zhouzaida/channel-distillation. | http://arxiv.org/pdf/2006.01683v1 | [
"Zaida Zhou",
"Chaoran Zhuge",
"Xinwei Guan",
"Wen Liu"
] | 2020-06-02T14:59:50Z | 2020-06-02T14:59:50Z |
2006.02392 | Data-driven learning of non-autonomous systems | We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method. | http://arxiv.org/pdf/2006.02392v1 | [
"Tong Qin",
"Zhen Chen",
"John Jakeman",
"Dongbin Xiu"
] | 2020-06-02T15:33:23Z | 2020-06-02T15:33:23Z |
1907.02998 | Self-supervised Learning of Distance Functions for Goal-Conditioned
Reinforcement Learning | Goal-conditioned policies are used in order to break down complex reinforcement learning (RL) problems by using subgoals, which can be defined either in state space or in a latent feature space. This can increase the efficiency of learning by using a curriculum, and also enables simultaneous learning and generalization across goals. A crucial requirement of goal-conditioned policies is to be able to determine whether the goal has been achieved. Having a notion of distance to a goal is thus a crucial component of this approach. However, it is not straightforward to come up with an appropriate distance, and in some tasks, the goal space may not even be known a priori. In this work we learn a distance-to-goal estimate which is computed in terms of the number of actions that would need to be carried out in a self-supervised approach. Our method solves complex tasks without prior domain knowledge in the online setting in three different scenarios in the context of goal-conditioned policies a) the goal space is the same as the state space b) the goal space is given but an appropriate distance is unknown and c) the state space is accessible, but only a subset of the state space represents desired goals, and this subset is known a priori. We also propose a goal-generation mechanism as a secondary contribution. | http://arxiv.org/pdf/1907.02998v2 | [
"Srinivas Venkattaramanujam",
"Eric Crawford",
"Thang Doan",
"Doina Precup"
] | 2020-06-02T15:42:15Z | 2019-07-05T19:00:14Z |
2006.01732 | Toward Optimal Probabilistic Active Learning Using a Bayesian Approach | Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims. | http://arxiv.org/pdf/2006.01732v1 | [
"Daniel Kottke",
"Marek Herde",
"Christoph Sandrock",
"Denis Huseljic",
"Georg Krempl",
"Bernhard Sick"
] | 2020-06-02T15:59:42Z | 2020-06-02T15:59:42Z |
1811.08674 | Graph Refinement based Airway Extraction using Mean-Field Networks and
Graph Neural Networks | Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives. | http://arxiv.org/pdf/1811.08674v2 | [
"Raghavendra Selvan",
"Thomas Kipf",
"Max Welling",
"Antonio Garcia-Uceda Juarez",
"Jesper H Pedersen",
"Jens Petersen",
"Marleen de Bruijne"
] | 2020-06-02T16:14:58Z | 2018-11-21T10:50:31Z |
2006.01752 | Performance metrics for intervention-triggering prediction models do not
reflect an expected reduction in outcomes from using the model | Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices. However, if these models are used to allocate interventions to patients, standard metrics calculated from retrospective data are only related to model utility (in terms of reductions in outcomes) under certain assumptions. When predictions are delivered repeatedly throughout time (e.g. in a patient encounter), the relationship between standard metrics and utility is further complicated. Several kinds of evaluations have been used in the literature, but it has not been clear what the target of estimation is in each evaluation. We synthesize these approaches, determine what is being estimated in each of them, and discuss under what assumptions those estimates are valid. We demonstrate our insights using simulated data as well as real data used in the design of an early warning system. Our theoretical and empirical results show that evaluations without interventional data either do not estimate meaningful quantities, require strong assumptions, or are limited to estimating best-case scenario bounds. | http://arxiv.org/pdf/2006.01752v1 | [
"Alejandro Schuler",
"Aashish Bhardwaj",
"Vincent Liu"
] | 2020-06-02T16:26:49Z | 2020-06-02T16:26:49Z |
2006.01782 | Temporally-Extended ε-Greedy Exploration | Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that, when applied to a broader set of domains, some sophisticated exploration methods are outperformed by simpler counterparts, such as {epsilon}-greedy. In this paper we propose an exploration algorithm that retains the simplicity of {epsilon}-greedy while reducing dithering. We build on a simple hypothesis: the main limitation of {epsilon}-greedy exploration is its lack of temporal persistence, which limits its ability to escape local optima. We propose a temporally extended form of {epsilon}-greedy that simply repeats the sampled action for a random duration. It turns out that, for many duration distributions, this suffices to improve exploration on a large set of domains. Interestingly, a class of distributions inspired by ecological models of animal foraging behaviour yields particularly strong performance. | http://arxiv.org/pdf/2006.01782v1 | [
"Will Dabney",
"Georg Ostrovski",
"André Barreto"
] | 2020-06-02T17:02:55Z | 2020-06-02T17:02:55Z |
2006.01785 | Geometric Graph Representations and Geometric Graph Convolutions for
Deep Learning on Three-Dimensional (3D) Graphs | The geometry of three-dimensional (3D) graphs, consisting of nodes and edges, plays a crucial role in many important applications. An excellent example is molecular graphs, whose geometry influences important properties of a molecule including its reactivity and biological activity. To facilitate the incorporation of geometry in deep learning on 3D graphs, we define three types of geometric graph representations: positional, angle-geometric and distance-geometric. For proof of concept, we use the distance-geometric graph representation for geometric graph convolutions. Further, to utilize standard graph convolution networks, we employ a simple edge weight / edge distance correlation scheme, whose parameters can be fixed using reference values or determined through Bayesian hyperparameter optimization. The results of geometric graph convolutions, for the ESOL and Freesol datasets, show significant improvement over those of standard graph convolutions. Our work demonstrates the feasibility and promise of incorporating geometry, using the distance-geometric graph representation, in deep learning on 3D graphs. | http://arxiv.org/pdf/2006.01785v1 | [
"Daniel T. Chang"
] | 2020-06-02T17:08:59Z | 2020-06-02T17:08:59Z |
2006.01789 | A probabilistic generative model for semi-supervised training of
coarse-grained surrogates and enforcing physical constraints through virtual
observables | The data-centric construction of inexpensive surrogates for fine-grained, physical models has been at the forefront of computational physics due to its significant utility in many-query tasks such as uncertainty quantification. Recent efforts have taken advantage of the enabling technologies from the field of machine learning (e.g. deep neural networks) in combination with simulation data. While such strategies have shown promise even in higher-dimensional problems, they generally require large amounts of training data even though the construction of surrogates is by definition a Small Data problem. Rather than employing data-based loss functions, it has been proposed to make use of the governing equations (in the simplest case at collocation points) in order to imbue domain knowledge in the training of the otherwise black-box-like interpolators. The present paper provides a flexible, probabilistic framework that accounts for physical structure and information both in the training objectives as well as in the surrogate model itself. We advocate a probabilistic (Bayesian) model in which equalities that are available from the physics (e.g. residuals, conservation laws) can be introduced as virtual observables and can provide additional information through the likelihood. We further advocate a generative model i.e. one that attempts to learn the joint density of inputs and outputs that is capable of making use of unlabeled data (i.e. only inputs) in a semi-supervised fashion in order to promote the discovery of lower-dimensional embeddings which are nevertheless predictive of the fine-grained model's output. | http://arxiv.org/abs/2006.01789v1 | [
"Maximilian Rixner",
"Phaedon-Stelios Koutsourelakis"
] | 2020-06-02T17:14:36Z | 2020-06-02T17:14:36Z |
2006.01795 | Shapley Value as Principled Metric for Structured Network Pruning | Structured pruning is a well-known technique to reduce the storage size and inference cost of neural networks. The usual pruning pipeline consists of ranking the network internal filters and activations with respect to their contributions to the network performance, removing the units with the lowest contribution, and fine-tuning the network to reduce the harm induced by pruning. Recent results showed that random pruning performs on par with other metrics, given enough fine-tuning resources. In this work, we show that this is not true on a low-data regime when fine-tuning is either not possible or not effective. In this case, reducing the harm caused by pruning becomes crucial to retain the performance of the network. First, we analyze the problem of estimating the contribution of hidden units with tools suggested by cooperative game theory and propose Shapley values as a principled ranking metric for this task. We compare with several alternatives proposed in the literature and discuss how Shapley values are theoretically preferable. Finally, we compare all ranking metrics on the challenging scenario of low-data pruning, where we demonstrate how Shapley values outperform other heuristics. | http://arxiv.org/pdf/2006.01795v1 | [
"Marco Ancona",
"Cengiz Öztireli",
"Markus Gross"
] | 2020-06-02T17:26:49Z | 2020-06-02T17:26:49Z |
2006.01816 | Age-Based Coded Computation for Bias Reduction in Distributed Learning | Coded computation can be used to speed up distributed learning in the presence of straggling workers. Partial recovery of the gradient vector can further reduce the computation time at each iteration; however, this can result in biased estimators, which may slow down convergence, or even cause divergence. Estimator bias will be particularly prevalent when the straggling behavior is correlated over time, which results in the gradient estimators being dominated by a few fast servers. To mitigate biased estimators, we design a $timely$ dynamic encoding framework for partial recovery that includes an ordering operator that changes the codewords and computation orders at workers over time. To regulate the recovery frequencies, we adopt an $age$ metric in the design of the dynamic encoding scheme. We show through numerical results that the proposed dynamic encoding strategy increases the timeliness of the recovered computations, which as a result, reduces the bias in model updates, and accelerates the convergence compared to the conventional static partial recovery schemes. | http://arxiv.org/pdf/2006.01816v1 | [
"Emre Ozfatura",
"Baturalp Buyukates",
"Deniz Gunduz",
"Sennur Ulukus"
] | 2020-06-02T17:51:11Z | 2020-06-02T17:51:11Z |
1901.03227 | Closed-form Expressions for Maximum Mean Discrepancy with Applications
to Wasserstein Auto-Encoders | The Maximum Mean Discrepancy (MMD) has found numerous applications in statistics and machine learning, most recently as a penalty in the Wasserstein Auto-Encoder (WAE). In this paper we compute closed-form expressions for estimating the Gaussian kernel based MMD between a given distribution and the standard multivariate normal distribution. This formula reveals a connection to the Baringhaus-Henze-Epps-Pulley (BHEP) statistic of the Henze-Zirkler test and provides further insights about the MMD. We introduce the standardized version of MMD as a penalty for the WAE training objective, allowing for a better interpretability of MMD values and more compatibility across different hyperparameter settings. Next, we propose using a version of batch normalization at the code layer; this has the benefits of making the kernel width selection easier, reducing the training effort, and preventing outliers in the aggregate code distribution. Our experiments on synthetic and real data show that the analytic formulation improves over the commonly used stochastic approximation of the MMD, and demonstrate that code normalization provides significant benefits when training WAEs. | http://arxiv.org/pdf/1901.03227v2 | [
"Raif M. Rustamov"
] | 2020-06-02T17:53:22Z | 2019-01-10T15:43:58Z |
2006.01854 | Event Arguments Extraction via Dilate Gated Convolutional Neural Network
with Enhanced Local Features | Event Extraction plays an important role in information-extraction to understand the world. Event extraction could be split into two subtasks: one is event trigger extraction, the other is event arguments extraction. However, the F-Score of event arguments extraction is much lower than that of event trigger extraction, i.e. in the most recent work, event trigger extraction achieves 80.7%, while event arguments extraction achieves only 58%. In pipelined structures, the difficulty of event arguments extraction lies in its lack of classification feature, and the much higher computation consumption. In this work, we proposed a novel Event Extraction approach based on multi-layer Dilate Gated Convolutional Neural Network (EE-DGCNN) which has fewer parameters. In addition, enhanced local information is incorporated into word features, to assign event arguments roles for triggers predicted by the first subtask. The numerical experiments demonstrated significant performance improvement beyond state-of-art event extraction approaches on real-world datasets. Further analysis of extraction procedure is presented, as well as experiments are conducted to analyze impact factors related to the performance improvement. | http://arxiv.org/pdf/2006.01854v1 | [
"Zhigang Kan",
"Linbo Qiao",
"Sen Yang",
"Feng Liu",
"Feng Huang"
] | 2020-06-02T18:05:34Z | 2020-06-02T18:05:34Z |
1905.13136 | Job Recommendation through Progression of Job Selection | Job recommendation has traditionally been treated as a filter-based match or as a recommendation based on the features of jobs and candidates as discrete entities. In this paper, we introduce a methodology where we leverage the progression of job selection by candidates using machine learning. Additionally, our recommendation is composed of several other sub-recommendations that contribute to at least one of a) making recommendations serendipitous for the end user b) overcoming cold-start for both candidates and jobs. One of the unique selling propositions of our methodology is the way we have used skills as embedded features and derived latent competencies from them, thereby attempting to expand the skills of candidates and jobs to achieve more coverage in the skill domain. We have deployed our model in a real-world job recommender system and have achieved the best click-through rate through a blended approach of machine-learned recommendations and other sub-recommendations. For recommending jobs through machine learning that forms a significant part of our recommendation, we achieve the best results through Bi-LSTM with attention. | http://arxiv.org/pdf/1905.13136v2 | [
"Amber Nigam",
"Aakash Roy",
"Arpan Saxena",
"Hartaran Singh"
] | 2020-06-02T18:24:31Z | 2019-05-28T14:36:48Z |
2001.06938 | Memory capacity of neural networks with threshold and ReLU activations | Overwhelming theoretical and empirical evidence shows that mildly overparametrized neural networks -- those with more connections than the size of the training data -- are often able to memorize the training data with $100%$ accuracy. This was rigorously proved for networks with sigmoid activation functions and, very recently, for ReLU activations. Addressing a 1988 open question of Baum, we prove that this phenomenon holds for general multilayered perceptrons, i.e. neural networks with threshold activation functions, or with any mix of threshold and ReLU activations. Our construction is probabilistic and exploits sparsity. | http://arxiv.org/pdf/2001.06938v2 | [
"Roman Vershynin"
] | 2020-06-02T18:38:18Z | 2020-01-20T01:54:21Z |
2006.04527 | Objective-Sensitive Principal Component Analysis for High-Dimensional
Inverse Problems | We present a novel approach for adaptive, differentiable parameterization of large-scale random fields. If the approach is coupled with any gradient-based optimization algorithm, it can be applied to a variety of optimization problems, including history matching. The developed technique is based on principal component analysis (PCA) but modifies a purely data-driven basis of principal components considering objective function behavior. To define an efficient encoding, Gradient-Sensitive PCA uses an objective function gradient with respect to model parameters. We propose computationally efficient implementations of the technique, and two of them are based on stationary perturbation theory (SPT). Optimality, correctness, and low computational costs of the new encoding approach are tested, verified, and discussed. Three algorithms for optimal parameter decomposition are presented and applied to an objective of 2D synthetic history matching. The results demonstrate improvements in encoding quality regarding objective function minimization and distributional patterns of the desired field. Possible applications and extensions are proposed. | http://arxiv.org/pdf/2006.04527v1 | [
"Maksim Elizarev",
"Andrei Mukhin",
"Aleksey Khlyupin"
] | 2020-06-02T18:51:17Z | 2020-06-02T18:51:17Z |
2006.00615 | Fully probabilistic quasar continua predictions near Lyman-α with
conditional neural spline flows | Measurement of the red damping wing of neutral hydrogen in quasar spectra provides a probe of the epoch of reionization in the early Universe. Such quantification requires precise and unbiased estimates of the intrinsic continua near Lyman-$alpha$ (Ly$alpha$), a challenging task given the highly variable Ly$alpha$ emission profiles of quasars. Here, we introduce a fully probabilistic approach to intrinsic continua prediction. We frame the problem as a conditional density estimation task and explicitly model the distribution over plausible blue-side continua ($1190 unicode{xC5} leq lambda_{text{rest}} < 1290 unicode{xC5}$) conditional on the red-side spectrum ($1290 unicode{xC5} leq lambda_{text{rest}} < 2900 unicode{xC5}$) using normalizing flows. Our approach achieves state-of-the-art precision and accuracy, allows for sampling one thousand plausible continua in less than a tenth of a second, and can natively provide confidence intervals on the blue-side continua via Monte Carlo sampling. We measure the damping wing effect in two $z>7$ quasars and estimate the volume-averaged neutral fraction of hydrogen from each, finding $bar{x}_text{HI}=0.304 pm 0.042$ for ULAS J1120+0641 ($z=7.09$) and $bar{x}_text{HI}=0.384 pm 0.133$ for ULAS J1342+0928 ($z=7.54$). | http://arxiv.org/pdf/2006.00615v2 | [
"David M. Reiman",
"John Tamanas",
"J. Xavier Prochaska",
"Dominika Ďurovčíková"
] | 2020-06-02T19:05:23Z | 2020-05-31T21:18:23Z |
2006.01892 | Finite Difference Neural Networks: Fast Prediction of Partial
Differential Equations | Discovering the underlying behavior of complex systems is an important topic in many science and engineering disciplines. In this paper, we propose a novel neural network framework, finite difference neural networks (FDNet), to learn partial differential equations from data. Specifically, our proposed finite difference inspired network is designed to learn the underlying governing partial differential equations from trajectory data, and to iteratively estimate the future dynamical behavior using only a few trainable parameters. We illustrate the performance (predictive power) of our framework on the heat equation, with and without noise and/or forcing, and compare our results to the Forward Euler method. Moreover, we show the advantages of using a Hessian-Free Trust Region method to train the network. | http://arxiv.org/pdf/2006.01892v1 | [
"Zheng Shi",
"Nur Sila Gulgec",
"Albert S. Berahas",
"Shamim N. Pakzad",
"Martin Takáč"
] | 2020-06-02T19:17:58Z | 2020-06-02T19:17:58Z |
2005.11603 | Geometric algorithms for predicting resilience and recovering damage in
neural networks | Biological neural networks have evolved to maintain performance despite significant circuit damage. To survive damage, biological network architectures have both intrinsic resilience to component loss and also activate recovery programs that adjust network weights through plasticity to stabilize performance. Despite the importance of resilience in technology applications, the resilience of artificial neural networks is poorly understood, and autonomous recovery algorithms have yet to be developed. In this paper, we establish a mathematical framework to analyze the resilience of artificial neural networks through the lens of differential geometry. Our geometric language provides natural algorithms that identify local vulnerabilities in trained networks as well as recovery algorithms that dynamically adjust networks to compensate for damage. We reveal striking vulnerabilities in commonly used image analysis networks, like MLP's and CNN's trained on MNIST and CIFAR10 respectively. We also uncover high-performance recovery paths that enable the same networks to dynamically re-adjust their parameters to compensate for damage. Broadly, our work provides procedures that endow artificial systems with resilience and rapid-recovery routines to enhance their integration with IoT devices as well as enable their deployment for critical applications. | http://arxiv.org/pdf/2005.11603v2 | [
"Guruprasad Raghavan",
"Jiayi Li",
"Matt Thomson"
] | 2020-06-02T19:20:49Z | 2020-05-23T21:13:26Z |
1910.01444 | Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit
Feedback | In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the learning of a recommender with biased feedback is one of the most challenging problems, as it is widely known that naive approaches under selection bias often lead to suboptimal results. A well-established solution for the problem is using propensity scoring techniques. The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of its propensity. However, the performance of the propensity-based unbiased estimation approach is often affected by choice of the propensity estimation model or the high variance problem. To overcome these limitations, we propose a model-agnostic meta-learning method inspired by the asymmetric tri-training framework for unsupervised domain adaptation. The proposed method utilizes two predictors to generate data with reliable pseudo-ratings and another predictor to make the final predictions. In a theoretical analysis, a propensity-independent upper bound of the true performance metric is derived, and it is demonstrated that the proposed method can minimize this bound. We conduct comprehensive experiments using public real-world datasets. The results suggest that the previous propensity-based methods are largely affected by the choice of propensity models and the variance problem caused by the inverse propensity weighting. Moreover, we show that the proposed meta-learning method is robust to these issues and can facilitate in developing effective recommendations from biased explicit feedback. | http://arxiv.org/pdf/1910.01444v6 | [
"Yuta Saito"
] | 2020-06-02T19:21:41Z | 2019-09-08T07:23:46Z |
2006.01898 | Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist
Clinicians with COVID-19 ECMO Planning | Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life. While early planning and surgical cannulation for ECMO can increase survival, clinicians report the lack of a risk score hinders these efforts. In this work, we leverage machine learning techniques to develop the PEER score, used to highlight critically ill patients with viral or unspecified pneumonia at high risk of mortality or decompensation in a subpopulation eligible for ECMO. The PEER score is validated on two large, publicly available critical care databases and predicts mortality at least as well as other existing risk scores. Stratifying our cohorts into low-risk and high-risk groups, we find that the high-risk group also has a higher proportion of decompensation indicators such as vasopressor and ventilator use. Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO. | http://arxiv.org/pdf/2006.01898v1 | [
"Helen Zhou",
"Cheng Cheng",
"Zachary C. Lipton",
"George H. Chen",
"Jeremy C. Weiss"
] | 2020-06-02T19:30:29Z | 2020-06-02T19:30:29Z |
2006.01916 | Quantifying the Effects of Prosody Modulation on User Engagement and
Satisfaction in Conversational Systems | As voice-based assistants such as Alexa, Siri, and Google Assistant become ubiquitous, users increasingly expect to maintain natural and informative conversations with such systems. However, for an open-domain conversational system to be coherent and engaging, it must be able to maintain the user's interest for extended periods, without sounding boring or annoying. In this paper, we investigate one natural approach to this problem, of modulating response prosody, i.e., changing the pitch and cadence of the response to indicate delight, sadness or other common emotions, as well as using pre-recorded interjections. Intuitively, this approach should improve the naturalness of the conversation, but attempts to quantify the effects of prosodic modulation on user satisfaction and engagement remain challenging. To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level. Our results indicate that the prosody modulation significantly increases both immediate and overall user satisfaction. However, since the effects vary across different domains, we verify that prosody modulations do not substitute for coherent, informative content of the responses. Together, our results provide useful tools and insights for improving the naturalness of responses in conversational systems. | http://arxiv.org/abs/2006.01916v1 | [
"Jason Ingyu Choi",
"Eugene Agichtein"
] | 2020-06-02T19:53:13Z | 2020-06-02T19:53:13Z |
2004.04072 | CNN-MoE based framework for classification of respiratory anomalies and
lung disease detection | This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect disease, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound analysis. Firstly, we carry out an extensive exploration of the effect of spectrogram type, spectral-time resolution, overlapped/non-overlapped windows, and data augmentation on final prediction accuracy. This leads us to propose a novel deep learning system, built on the proposed framework, which outperforms current state-of-the-art methods. Finally, we apply a Teacher-Student scheme to achieve a trade-off between model performance and model complexity which additionally helps to increase the potential of the proposed framework for building real-time applications. | http://arxiv.org/pdf/2004.04072v2 | [
"Lam Pham",
"Huy Phan",
"Ramaswamy Palaniappan",
"Alfred Mertins",
"Ian McLoughlin"
] | 2020-06-02T19:55:28Z | 2020-04-04T21:45:06Z |
2006.01921 | Offline and Online Satisfaction Prediction in Open-Domain Conversational
Systems | Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement. | http://arxiv.org/abs/2006.01921v1 | [
"Jason Ingyu Choi",
"Ali Ahmadvand",
"Eugene Agichtein"
] | 2020-06-02T20:04:56Z | 2020-06-02T20:04:56Z |
2006.01938 | Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased
Proximities in Word Embeddings | Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology which not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighbouring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution). | http://arxiv.org/pdf/2006.01938v1 | [
"Vaibhav Kumar",
"Tenzin Singhay Bhotia",
"Vaibhav Kumar",
"Tanmoy Chakraborty"
] | 2020-06-02T20:50:43Z | 2020-06-02T20:50:43Z |
2006.01945 | Continual Learning of Predictive Models in Video Sequences via
Variational Autoencoders | This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial Variational Autoencoder, together with a set of fully connected neural networks are utilized to respectively learn the appearance of video frames and their dynamics at the latent space level. By employing an adapted Markov Jump Particle Filter, the proposed method recognizes new situations and integrates them as predictive models avoiding catastrophic forgetting of previously learned tasks. For evaluating the proposed method, this article uses video sequences from a vehicle that performs different tasks in a controlled environment. | http://arxiv.org/pdf/2006.01945v1 | [
"Damian Campo",
"Giulia Slavic",
"Mohamad Baydoun",
"Lucio Marcenaro",
"Carlo Regazzoni"
] | 2020-06-02T21:17:38Z | 2020-06-02T21:17:38Z |
2006.01963 | Multi-level Graph Convolutional Networks for Cross-platform Anchor Link
Prediction | Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network topology, which brings researchers into an insoluble dilemma of model selection. In this paper, to address this problem, we propose a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner. The proposed method overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information. Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks. Extensive experiments have been conducted on two large-scale real-life social networks. The experimental results demonstrate that the proposed method outperforms the state-of-the-art models with a big margin. | http://arxiv.org/pdf/2006.01963v1 | [
"Hongxu Chen",
"Hongzhi Yin",
"Xiangguo Sun",
"Tong Chen",
"Bogdan Gabrys",
"Katarzyna Musial"
] | 2020-06-02T22:01:27Z | 2020-06-02T22:01:27Z |
2005.09669 | Exponential ergodicity of mirror-Langevin diffusions | Motivated by the problem of sampling from ill-conditioned log-concave distributions, we give a clean non-asymptotic convergence analysis of mirror-Langevin diffusions as introduced in Zhang et al. (2020). As a special case of this framework, we propose a class of diffusions called Newton-Langevin diffusions and prove that they converge to stationarity exponentially fast with a rate which not only is dimension-free, but also has no dependence on the target distribution. We give an application of this result to the problem of sampling from the uniform distribution on a convex body using a strategy inspired by interior-point methods. Our general approach follows the recent trend of linking sampling and optimization and highlights the role of the chi-squared divergence. In particular, it yields new results on the convergence of the vanilla Langevin diffusion in Wasserstein distance. | http://arxiv.org/pdf/2005.09669v2 | [
"Sinho Chewi",
"Thibaut Le Gouic",
"Chen Lu",
"Tyler Maunu",
"Philippe Rigollet",
"Austin J. Stromme"
] | 2020-06-02T22:39:02Z | 2020-05-19T18:00:52Z |
2006.03040 | Deep learning-based reduced order models in cardiac electrophysiology | Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the formulation and numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depolarization cycle occurring at every heart beat that models the time evolution of the electrical potential across the cell membrane, as well as a set of ionic variables. Multiple solutions of these systems, corresponding to different model inputs, are required to evaluate outputs of clinical interest, such as activation maps and action potential duration. More importantly, these models feature coherent structures that propagate over time, such as wavefronts. These systems can hardly be reduced to lower dimensional problems by conventional reduced order models (ROMs) such as, e.g., the reduced basis (RB) method. This is primarily due to the low regularity of the solution manifold (with respect to the problem parameters) as well as to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To overcome this difficulty, in this paper we propose a new, nonlinear approach which exploits deep learning (DL) algorithms to obtain accurate and efficient ROMs, whose dimensionality matches the number of system parameters. Our DL approach combines deep feedforward neural networks (NNs) and convolutional autoencoders (AEs). We show that the proposed DL-ROM framework can efficiently provide solutions to parametrized electrophysiology problems, thus enabling multi-scenario analysis in pathological cases. We investigate three challenging test cases in cardiac electrophysiology and prove that DL-ROM outperforms classical projection-based ROMs. | http://arxiv.org/abs/2006.03040v1 | [
"Stefania Fresca",
"Andrea Manzoni",
"Luca Dedè",
"Alfio Quarteroni"
] | 2020-06-02T23:05:03Z | 2020-06-02T23:05:03Z |
2006.05676 | Position Masking for Language Models | Masked language modeling (MLM) pre-training models such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. This is an effective technique which has led to good results on all NLP benchmarks. We propose to expand upon this idea by masking the positions of some tokens along with the masked input token ids. We follow the same standard approach as BERT masking a percentage of the tokens positions and then predicting their original values using an additional fully connected classifier stage. This approach has shown good performance gains (.3% improvement) for the SQUAD additional improvement in convergence times. For the Graphcore IPU the convergence of BERT Base with position masking requires only 50% of the tokens from the original BERT paper. | http://arxiv.org/pdf/2006.05676v1 | [
"Andy Wagner",
"Tiyasa Mitra",
"Mrinal Iyer",
"Godfrey Da Costa",
"Marc Tremblay"
] | 2020-06-02T23:40:41Z | 2020-06-02T23:40:41Z |
2006.01983 | Quantifying the Uncertainty in Model Parameters Using Gaussian
Process-Based Markov Chain Monte Carlo: An Application to Cardiac
Electrophysiological Models | Estimation of patient-specific model parameters is important for personalized modeling, although sparse and noisy clinical data can introduce significant uncertainty in the estimated parameter values. This importance source of uncertainty, if left unquantified, will lead to unknown variability in model outputs that hinder their reliable adoptions. Probabilistic estimation model parameters, however, remains an unresolved challenge because standard Markov Chain Monte Carlo sampling requires repeated model simulations that are computationally infeasible. A common solution is to replace the simulation model with a computationally-efficient surrogate for a faster sampling. However, by sampling from an approximation of the exact posterior probability density function (pdf) of the parameters, the efficiency is gained at the expense of sampling accuracy. In this paper, we address this issue by integrating surrogate modeling into Metropolis Hasting (MH) sampling of the exact posterior pdfs to improve its acceptance rate. It is done by first quickly constructing a Gaussian process (GP) surrogate of the exact posterior pdfs using deterministic optimization. This efficient surrogate is then used to modify commonly-used proposal distributions in MH sampling such that only proposals accepted by the surrogate will be tested by the exact posterior pdf for acceptance/rejection, reducing unnecessary model simulations at unlikely candidates. Synthetic and real-data experiments using the presented method show a significant gain in computational efficiency without compromising the accuracy. In addition, insights into the non-identifiability and heterogeneity of tissue properties can be gained from the obtained posterior distributions. | http://arxiv.org/abs/2006.01983v1 | [
"Jwala Dhamala",
"John L. Sapp",
"B. Milan Horácek",
"Linwei Wang"
] | 2020-06-02T23:48:15Z | 2020-06-02T23:48:15Z |
2006.01991 | Detecting and Understanding Real-World Differential Performance Bugs in
Machine Learning Libraries | Programming errors that degrade the performance of systems are widespread, yet there is little tool support for analyzing these bugs. We present a method based on differential performance analysis---we find inputs for which the performance varies widely, despite having the same size. To ensure that the differences in the performance are robust (i.e. hold also for large inputs), we compare the performance of not only single inputs, but of classes of inputs, where each class has similar inputs parameterized by their size. Thus, each class is represented by a performance function from the input size to performance. Importantly, we also provide an explanation for why the performance differs in a form that can be readily used to fix a performance bug. The two main phases in our method are discovery with fuzzing and explanation with decision tree classifiers, each of which is supported by clustering. First, we propose an evolutionary fuzzing algorithm to generate inputs. For this fuzzing task, the unique challenge is that we not only need the input class with the worst performance, but rather a set of classes exhibiting differential performance. We use clustering to merge similar input classes which significantly improves the efficiency of our fuzzer. Second, we explain the differential performance in terms of program inputs and internals. We adapt discriminant learning approaches with clustering and decision trees to localize suspicious code regions. We applied our techniques to a set of applications. On a set of micro-benchmarks, we show that our approach outperforms state-of-the-art fuzzers in finding inputs to characterize the differential performance. On a set of case-studies, we discover and explain multiple performance bugs in popular machine learning frameworks. Four of these bugs, reported first in this paper, have since been fixed by the developers. | http://arxiv.org/pdf/2006.01991v1 | [
"Saeid Tizpaz-Niari",
"Pavol Cerný",
"Ashutosh Trivedi"
] | 2020-06-03T00:23:06Z | 2020-06-03T00:23:06Z |
2006.01993 | PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in
High Energy Physics | Rapid advancement of machine learning solutions has often coincided with the production of a test public data set. Such datasets reduce the largest barrier to entry for tackling a problem -- procuring data -- while also providing a benchmark to compare different solutions. Furthermore, large datasets have been used to train high-performing feature finders which are then used in new approaches to problems beyond that initially defined. In order to encourage the rapid development in the analysis of data collected using liquid argon time projection chambers, a class of particle detectors used in high energy physics experiments, we have produced the PILArNet, first 2D and 3D open dataset to be used for a couple of key analysis tasks. The initial dataset presented in this paper contains 300,000 samples simulated and recorded in three different volume sizes. The dataset is stored efficiently in sparse 2D and 3D matrix format with auxiliary information about simulated particles in the volume, and is made available for public research use. In this paper we describe the dataset, tasks, and the method used to procure the sample. | http://arxiv.org/pdf/2006.01993v1 | [
"Corey Adams",
"Kazuhiro Terao",
"Taritree Wongjirad"
] | 2020-06-03T00:36:04Z | 2020-06-03T00:36:04Z |
2006.01997 | Automatic Text Summarization of COVID-19 Medical Research Articles using
BERT and GPT-2 | With the COVID-19 pandemic, there is a growing urgency for medical community to keep up with the accelerating growth in the new coronavirus-related literature. As a result, the COVID-19 Open Research Dataset Challenge has released a corpus of scholarly articles and is calling for machine learning approaches to help bridging the gap between the researchers and the rapidly growing publications. Here, we take advantage of the recent advances in pre-trained NLP models, BERT and OpenAI GPT-2, to solve this challenge by performing text summarization on this dataset. We evaluate the results using ROUGE scores and visual inspection. Our model provides abstractive and comprehensive information based on keywords extracted from the original articles. Our work can help the the medical community, by providing succinct summaries of articles for which the abstract are not already available. | http://arxiv.org/pdf/2006.01997v1 | [
"Virapat Kieuvongngam",
"Bowen Tan",
"Yiming Niu"
] | 2020-06-03T00:54:44Z | 2020-06-03T00:54:44Z |
2006.02001 | Learning with CVaR-based feedback under potentially heavy tails | We study learning algorithms that seek to minimize the conditional value-at-risk (CVaR), when all the learner knows is that the losses incurred may be heavy-tailed. We begin by studying a general-purpose estimator of CVaR for potentially heavy-tailed random variables, which is easy to implement in practice, and requires nothing more than finite variance and a distribution function that does not change too fast or slow around just the quantile of interest. With this estimator in hand, we then derive a new learning algorithm which robustly chooses among candidates produced by stochastic gradient-driven sub-processes. For this procedure we provide high-probability excess CVaR bounds, and to complement the theory we conduct empirical tests of the underlying CVaR estimator and the learning algorithm derived from it. | http://arxiv.org/pdf/2006.02001v1 | [
"Matthew J. Holland",
"El Mehdi Haress"
] | 2020-06-03T01:08:29Z | 2020-06-03T01:08:29Z |
2006.02003 | Open-Set Recognition with Gaussian Mixture Variational Autoencoders | In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 improvement of 29.5%, through extensive experiments aided by analytical results. | http://arxiv.org/pdf/2006.02003v1 | [
"Alexander Cao",
"Yuan Luo",
"Diego Klabjan"
] | 2020-06-03T01:15:19Z | 2020-06-03T01:15:19Z |
1908.06869 | XSP: Across-Stack Profiling and Analysis of Machine Learning Models on
GPUs | There has been a rapid proliferation of machine learning/deep learning (ML) models and wide adoption of them in many application domains. This has made profiling and characterization of ML model performance an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible system to serve ML models with the target latency, throughput, cost, and energy requirements while maximizing resource utilization. Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack). Existing profiling tools are disjoint, however, and only focus on profiling within a particular level of the stack, which limits the thoroughness and usefulness of the profiling results. This paper proposes XSP - an across-stack profiling design that gives a holistic and hierarchical view of ML model execution. XSP leverages distributed tracing to aggregate and correlates profile data from different sources. XSP introduces a leveled and iterative measurement approach that accurately captures the latencies at all levels of the HW/SW stack in spite of the profiling overhead. We couple the profiling design with an automated analysis pipeline to systematically analyze 65 state-of-the-art ML models. We demonstrate that XSP provides insights which would be difficult to discern otherwise. | http://arxiv.org/abs/1908.06869v3 | [
"Cheng Li",
"Abdul Dakkak",
"Jinjun Xiong",
"Wei Wei",
"Lingjie Xu",
"Wen-mei Hwu"
] | 2020-06-03T01:31:35Z | 2019-08-19T15:05:29Z |
2006.02014 | Norm-Based Curriculum Learning for Neural Machine Translation | A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method. We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence. The norm-based sentence difficulty takes the advantages of both linguistically motivated and model-based sentence difficulties. It is easy to determine and contains learning-dependent features. The norm-based model competence makes NMT learn the curriculum in a fully automated way, while the norm-based sentence weight further enhances the learning of the vector representation of the NMT. Experimental results for the WMT'14 English-German and WMT'17 Chinese-English translation tasks demonstrate that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x). | http://arxiv.org/pdf/2006.02014v1 | [
"Xuebo Liu",
"Houtim Lai",
"Derek F. Wong",
"Lidia S. Chao"
] | 2020-06-03T02:22:00Z | 2020-06-03T02:22:00Z |
2004.13013 | Harnessing adversarial examples with a surprisingly simple defense | I introduce a very simple method to defend against adversarial examples. The basic idea is to raise the slope of the ReLU function at the test time. Experiments over MNIST and CIFAR-10 datasets demonstrate the effectiveness of the proposed defense against a number of strong attacks in both untargeted and targeted settings. While perhaps not as effective as the state of the art adversarial defenses, this approach can provide insights to understand and mitigate adversarial attacks. It can also be used in conjunction with other defenses. | http://arxiv.org/pdf/2004.13013v3 | [
"Ali Borji"
] | 2020-06-03T02:52:54Z | 2020-04-26T03:09:42Z |
2006.02043 | Hierarchical forecast reconciliation with machine learning | Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns. | http://arxiv.org/pdf/2006.02043v1 | [
"Evangelos Spiliotis",
"Mahdi Abolghasemi",
"Rob J Hyndman",
"Fotios Petropoulos",
"Vassilios Assimakopoulos"
] | 2020-06-03T04:49:39Z | 2020-06-03T04:49:39Z |
1911.06311 | Sato: Contextual Semantic Type Detection in Tables | Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection approaches either perform poorly with dirty data, support only a limited number of semantic types, fail to incorporate the table context of columns or rely on large sample sizes for training data. We introduce Sato, a hybrid machine learning model to automatically detect the semantic types of columns in tables, exploiting the signals from the context as well as the column values. Sato combines a deep learning model trained on a large-scale table corpus with topic modeling and structured prediction to achieve support-weighted and macro average F1 scores of 0.925 and 0.735, respectively, exceeding the state-of-the-art performance by a significant margin. We extensively analyze the overall and per-type performance of Sato, discussing how individual modeling components, as well as feature categories, contribute to its performance. | http://arxiv.org/pdf/1911.06311v3 | [
"Dan Zhang",
"Yoshihiko Suhara",
"Jinfeng Li",
"Madelon Hulsebos",
"Çağatay Demiralp",
"Wang-Chiew Tan"
] | 2020-06-03T04:54:28Z | 2019-11-14T18:51:59Z |
1912.11333 | Audio-based automatic mating success prediction of giant pandas | Giant pandas, stereotyped as silent animals, make significantly more vocal sounds during breeding season, suggesting that sounds are essential for coordinating their reproduction and expression of mating preference. Previous biological studies have also proven that giant panda sounds are correlated with mating results and reproduction. This paper makes the first attempt to devise an automatic method for predicting mating success of giant pandas based on their vocal sounds. Given an audio sequence of mating giant pandas recorded during breeding encounters, we first crop out the segments with vocal sound of giant pandas, and normalize its magnitude, and length. We then extract acoustic features from the audio segment and feed the features into a deep neural network, which classifies the mating into success or failure. The proposed deep neural network employs convolution layers followed by bidirection gated recurrent units to extract vocal features, and applies attention mechanism to force the network to focus on most relevant features. Evaluation experiments on a data set collected during the past nine years obtain promising results, proving the potential of audio-based automatic mating success prediction methods in assisting giant panda reproduction. | http://arxiv.org/pdf/1912.11333v3 | [
"WeiRan Yan",
"MaoLin Tang",
"Qijun Zhao",
"Peng Chen",
"Dunwu Qi",
"Rong Hou",
"Zhihe Zhang"
] | 2020-06-03T06:30:54Z | 2019-12-24T16:08:48Z |
2006.02064 | Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator
Analysis for Short-term Precipitation Forecasting | With the accumulation of meteorological big data, data-driven models for short-term precipitation forecasting have shown increasing promise. We focus on Koopman operator analysis, which is a data-driven scheme to discover governing laws in observed data. We propose a method to apply this scheme to phenomena accompanying advection currents such as precipitation. The proposed method decomposes time evolutions of the phenomena between advection currents under a velocity field and changes in physical quantities under Lagrangian coordinates. The advection currents are estimated by kinematic analysis, and the changes in physical quantities are estimated by Koopman operator analysis. The proposed method is applied to actual precipitation distribution data, and the results show that the development and decay of precipitation are properly captured relative to conventional methods and that stable predictions over long periods are possible. | http://arxiv.org/pdf/2006.02064v1 | [
"Shitao Zheng",
"Takashi Miyamoto",
"Koyuru Iwanami",
"Shingo Shimizu",
"Ryohei Kato"
] | 2020-06-03T06:32:54Z | 2020-06-03T06:32:54Z |
2006.01672 | Explaining the distribution of energy consumption at slow charging
infrastructure for electric vehicles from socio-economic data | Here, we develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. To gain a basic insight, we analysed the probabilistic distribution of energy consumption and its relation to indicators characterizing charging events. We collected geospatial datasets and utilizing statistical methods for data pre-processing, we prepared features modelling the spatial context in which the charging infrastructure operates. To enhance the statistical reliability of results, we applied the bootstrap method together with the Lasso method that combines regression with variable selection ability. We evaluate the statistical distributions of the selected regression coefficients. We identified the most influential features correlated with energy consumption, indicating that the spatial context of the charging infrastructure affects its utilization pattern. Many of these features are related to the economic prosperity of residents. Application of the methodology to a specific class of charging infrastructure enables the differentiation of selected features, e.g. by the used rollout strategy. Overall, the paper demonstrates the application of statistical methodologies to energy data and provides insights on factors potentially shaping the energy consumption that could be utilized when developing models to inform charging infrastructure deployment and planning of power grids. | http://arxiv.org/pdf/2006.01672v2 | [
"Milan Straka",
"Rui Carvalho",
"Gijs van der Poel",
"Ľuboš Buzna"
] | 2020-06-03T06:56:58Z | 2020-06-02T14:44:52Z |
1910.09998 | Learning Resilient Behaviors for Navigation Under Uncertainty | Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertainty-aware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties. | http://arxiv.org/pdf/1910.09998v3 | [
"Tingxiang Fan",
"Pinxin Long",
"Wenxi Liu",
"Jia Pan",
"Ruigang Yang",
"Dinesh Manocha"
] | 2020-06-03T07:15:15Z | 2019-10-22T14:15:20Z |
2006.02098 | GFPNet: A Deep Network for Learning Shape Completion in Generic Fitted
Primitives | In this paper, we propose an object reconstruction apparatus that uses the so-called Generic Primitives (GP) to complete shapes. A GP is a 3D point cloud depicting a generalized shape of a class of objects. To reconstruct the objects in a scene we first fit a GP onto each occluded object to obtain an initial raw structure. Secondly, we use a model-based deformation technique to fold the surface of the GP over the occluded object. The deformation model is encoded within the layers of a Deep Neural Network (DNN), coined GFPNet. The objective of the network is to transfer the particularities of the object from the scene to the raw volume represented by the GP. We show that GFPNet competes with state of the art shape completion methods by providing performance results on the ModelNet and KITTI benchmarking datasets. | http://arxiv.org/pdf/2006.02098v1 | [
"Tiberiu Cocias",
"Alexandru Razvant",
"Sorin Grigorescu"
] | 2020-06-03T08:29:27Z | 2020-06-03T08:29:27Z |
2006.02105 | Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian
Optimization and Tuning Rules | Deep learning techniques play an increasingly important role in industrial and research environments due to their outstanding results. However, the large number of hyper-parameters to be set may lead to errors if they are set manually. The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization. The first two methods are expensive because they try, respectively, all possible combinations and random combinations of hyper-parameters. Bayesian Optimization, instead, builds a surrogate model of the objective function, quantifies the uncertainty in the surrogate using Gaussian Process Regression and uses an acquisition function to decide where to sample the new set of hyper-parameters. This work faces the field of Hyper-Parameters Optimization (HPO). The aim is to improve Bayesian Optimization applied to Deep Neural Networks. For this goal, we build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets and use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper-parameter search space to select a better combination. | http://arxiv.org/pdf/2006.02105v1 | [
"Michele Fraccaroli",
"Evelina Lamma",
"Fabrizio Riguzzi"
] | 2020-06-03T08:53:48Z | 2020-06-03T08:53:48Z |
1901.09203 | ACNN: a Full Resolution DCNN for Medical Image Segmentation | Deep Convolutional Neural Networks (DCNNs) are used extensively in medical image segmentation and hence 3D navigation for robot-assisted Minimally Invasive Surgeries (MISs). However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, a new and full resolution DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Instance Normalization (IN) is proposed. Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve higher segmentation Intersection over Unions (IoUs) than U-Net and Deeplabv3+, but with reduced trainable parameters. | http://arxiv.org/pdf/1901.09203v4 | [
"Xiao-Yun Zhou",
"Jian-Qing Zheng",
"Peichao Li",
"Guang-Zhong Yang"
] | 2020-06-03T09:02:37Z | 2019-01-26T12:41:05Z |
2002.08258 | Knapsack Pruning with Inner Distillation | Neural network pruning reduces the computational cost of an over-parameterized network to improve its efficiency. Popular methods vary from $ell_1$-norm sparsification to Neural Architecture Search (NAS). In this work, we propose a novel pruning method that optimizes the final accuracy of the pruned network and distills knowledge from the over-parameterized parent network's inner layers. To enable this approach, we formulate the network pruning as a Knapsack Problem which optimizes the trade-off between the importance of neurons and their associated computational cost. Then we prune the network channels while maintaining the high-level structure of the network. The pruned network is fine-tuned under the supervision of the parent network using its inner network knowledge, a technique we refer to as the Inner Knowledge Distillation. Our method leads to state-of-the-art pruning results on ImageNet, CIFAR-10 and CIFAR-100 using ResNet backbones. To prune complex network structures such as convolutions with skip-links and depth-wise convolutions, we propose a block grouping approach to cope with these structures. Through this we produce compact architectures with the same FLOPs as EfficientNet-B0 and MobileNetV3 but with higher accuracy, by $1%$ and $0.3%$ respectively on ImageNet, and faster runtime on GPU. | http://arxiv.org/pdf/2002.08258v3 | [
"Yonathan Aflalo",
"Asaf Noy",
"Ming Lin",
"Itamar Friedman",
"Lihi Zelnik"
] | 2020-06-03T10:09:33Z | 2020-02-19T16:04:48Z |
1911.00926 | Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural
Computer Architecture | A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-augmented networks, that is inspired by the von Neumann and Harvard architectures of modern computers. This architecture enables the learning of abstract algorithmic solutions via Evolution Strategies in a reinforcement learning setting. Applied to Sokoban, sliding block puzzle and robotic manipulation tasks, we show that the architecture can learn algorithmic solutions with strong generalization and abstraction: scaling to arbitrary task configurations and complexities, and being independent of both the data representation and the task domain. | http://arxiv.org/pdf/1911.00926v2 | [
"Daniel Tanneberg",
"Elmar Rueckert",
"Jan Peters"
] | 2020-06-03T11:21:39Z | 2019-10-30T17:02:13Z |
2006.02174 | CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language
Learning | Approaches to Grounded Language Learning typically focus on a single task-based final performance measure that may not depend on desirable properties of the learned hidden representations, such as their ability to predict salient attributes or to generalise to unseen situations. To remedy this, we present GROLLA, an evaluation framework for Grounded Language Learning with Attributes with three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation. We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations, in particular concerning attribute grounding. To this end, we extend the original GuessWhat?! dataset by including a semantic layer on top of the perceptual one. Specifically, we enrich the VisualGenome scene graphs associated with the GuessWhat?! images with abstract and situated attributes. By using diagnostic classifiers, we show that current models learn representations that are not expressive enough to encode object attributes (average F1 of 44.27). In addition, they do not learn strategies nor representations that are robust enough to perform well when novel scenes or objects are involved in gameplay (zero-shot best accuracy 50.06%). | http://arxiv.org/pdf/2006.02174v1 | [
"Alessandro Suglia",
"Ioannis Konstas",
"Andrea Vanzo",
"Emanuele Bastianelli",
"Desmond Elliott",
"Stella Frank",
"Oliver Lemon"
] | 2020-06-03T11:21:42Z | 2020-06-03T11:21:42Z |
2006.02175 | Near-Tight Margin-Based Generalization Bounds for Support Vector
Machines | Support Vector Machines (SVMs) are among the most fundamental tools for binary classification. In its simplest formulation, an SVM produces a hyperplane separating two classes of data using the largest possible margin to the data. The focus on maximizing the margin has been well motivated through numerous generalization bounds. In this paper, we revisit and improve the classic generalization bounds in terms of margins. Furthermore, we complement our new generalization bound by a nearly matching lower bound, thus almost settling the generalization performance of SVMs in terms of margins. | http://arxiv.org/pdf/2006.02175v1 | [
"Allan Grønlund",
"Lior Kamma",
"Kasper Green Larsen"
] | 2020-06-03T11:22:37Z | 2020-06-03T11:22:37Z |
1806.06887 | The Minimax Learning Rates of Normal and Ising Undirected Graphical
Models | Let $G$ be an undirected graph with $m$ edges and $d$ vertices. We show that $d$-dimensional Ising models on $G$ can be learned from $n$ i.i.d. samples within expected total variation distance some constant factor of $min{1, sqrt{(m + d)/n}}$, and that this rate is optimal. We show that the same rate holds for the class of $d$-dimensional multivariate normal undirected graphical models with respect to $G$. We also identify the optimal rate of $min{1, sqrt{m/n}}$ for Ising models with no external magnetic field. | http://arxiv.org/pdf/1806.06887v3 | [
"Luc Devroye",
"Abbas Mehrabian",
"Tommy Reddad"
] | 2020-06-03T12:43:33Z | 2018-06-18T18:46:15Z |
2006.02244 | SimPool: Towards Topology Based Graph Pooling with Structural Similarity
Features | Deep learning methods for graphs have seen rapid progress in recent years with much focus awarded to generalising Convolutional Neural Networks (CNN) to graph data. CNNs are typically realised by alternating convolutional and pooling layers where the pooling layers subsample the grid and exchange spatial or temporal resolution for increased feature dimensionality. Whereas the generalised convolution operator for graphs has been studied extensively and proven useful, hierarchical coarsening of graphs is still challenging since nodes in graphs have no spatial locality and no natural order. This paper proposes two main contributions, the first is a differential module calculating structural similarity features based on the adjacency matrix. These structural similarity features may be used with various algorithms however in this paper the focus and the second main contribution is on integrating these features with a revisited pooling layer DiffPool arXiv:1806.08804 to propose a pooling layer referred to as SimPool. This is achieved by linking the concept of network reduction by means of structural similarity in graphs with the concept of hierarchical localised pooling. Experimental results demonstrate that as part of an end-to-end Graph Neural Network architecture SimPool calculates node cluster assignments that functionally resemble more to the locality preserving pooling operations used by CNNs that operate on local receptive fields in the standard grid. Furthermore the experimental results demonstrate that these features are useful in inductive graph classification tasks with no increase to the number of parameters. | http://arxiv.org/pdf/2006.02244v1 | [
"Yaniv Shulman"
] | 2020-06-03T12:51:57Z | 2020-06-03T12:51:57Z |
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