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9,100 | SHE: A Fast and Accurate Deep Neural Network for Encrypted Data∗ Qian Lou Indiana University Bloomington louqian@iu.edu Lei Jiang Indiana University Bloomington jiang60@iu.edu Abstract Homomorphic Encryption (HE) is one of the most promising security solutions to emerging Machine Learning as a Servi... | 2019 | 479 |
9,101 | A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits Wen-Hao Zhang1,2, Si Wu3, Brent Doiron2, Tai Sing Lee1 wenhao.zhang@pitt.edu; siwu@pku.edu.cn; bdoiron@pitt.edu; tai@cnbc.cmu.edu 1Center for the Neural Basis of Cognition, Carnegie Mellon University. 2Department of Mathem... | 2019 | 48 |
9,102 | Non-Cooperative Inverse Reinforcement Learning Xiangyuan Zhang Kaiqing Zhang Erik Miehling Tamer Bas¸ar Coordinated Science Laboratory University of Illinois at Urbana-Champaign {xz7,kzhang66,miehling,basar1}@illinois.edu Abstract Making decisions in the presence of a strategic opponent requires one t... | 2019 | 480 |
9,103 | Competitive Gradient Descent Florian Schäfer Computing and Mathematical Sciences California Institute of Technology Pasadena, CA 91125 florian.schaefer@caltech.edu Anima Anandkumar Computing and Mathematical Sciences California Institute of Technology Pasadena, CA 91125 anima@caltech.edu Abstract ... | 2019 | 481 |
9,104 | Learning in Generalized Linear Contextual Bandits with Stochastic Delays Zhengyuan Zhou1,2⇤, Renyuan Xu3⇤and Jose Blanchet4 1 Department of Electrical Engineering, Stanford University 2 Bytedance Inc. 3 Department of Industrial Engineering and Operations Research, UC Berkeley 4 Department of Management Scie... | 2019 | 482 |
9,105 | Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration Jianchun Chen ∗ NYU Multimedia and Visual Computing Lab New York University Brooklyn, NY 11201 jc7009@nyu.edu Lingjing Wang ∗ NYU Multimedia and Visual Computing Lab New York University Brooklyn, NY 11201 lw... | 2019 | 483 |
9,106 | On the Calibration of Multiclass Classification with Rejection Chenri Ni1 Nontawat Charoenphakdee1,2 Junya Honda1,2 Masashi Sugiyama2,1 1 The University of Tokyo, Japan 2 RIKEN Center for Advanced Intelligence Project, Japan {nichenri, nontawat}@ms.k.u-tokyo.ac.jp {jhonda, sugi}@k.u-tokyo.ac.jp Abstr... | 2019 | 484 |
9,107 | Point-Voxel CNN for Efficient 3D Deep Learning Zhijian Liu∗ MIT Haotian Tang∗ Shanghai Jiao Tong University Yujun Lin MIT Song Han MIT Abstract We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. ... | 2019 | 485 |
9,108 | Importance Weighted Hierarchical Variational Inference Artem Sobolev Samsung AI Center Moscow, Russia asobolev@bayesgroup.ru Dmitry Vetrov Samsung AI Center Moscow, Russia NRU HSE∗, Moscow, Russia Abstract Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiv... | 2019 | 486 |
9,109 | Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay Frederic Koehler Department of Mathematics Massachusetts Institute of Technology Cambridge, MA 02141 fkoehler@mit.edu Abstract Belief propagation is a fundamental message-passing algorithm for probabilistic reasoning and... | 2019 | 487 |
9,110 | ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization Xiangyi Chen1,∗ Sijia Liu2,∗Kaidi Xu3,∗ Xingguo Li4,∗ Xue Lin3 Mingyi Hong1 David Cox2 1University of Minnesota, USA 2MIT-IBM Watson AI Lab, IBM Research, USA 3Northeastern University, USA 4Princeton University, USA Abstr... | 2019 | 488 |
9,111 | U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging Mathias Perslev Department of Computer Science University of Copenhagen map@di.ku.dk Michael Hejselbak Jensen Department of Computer Science University of Copenhagen mhejselbak@gmail.com Sune Darkner Depart... | 2019 | 489 |
9,112 | The Geometry of Deep Networks: Power Diagram Subdivision Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk Rice University Houston, Texas, USA Abstract We study the geometry of deep (neural) networks (DNs) with piecewise affine and convex nonlinearities. The layers of such DNs hav... | 2019 | 49 |
9,113 | Meta-Curvature Eunbyung Park Department of Computer Science University of North Carolina at Chapel Hill eunbyung@cs.unc.edu Junier B. Oliva Department of Computer Science University of North Carolina at Chapel Hill joliva@cs.unc.edu Abstract We propose meta-curvature (MC), a framework to learn curva... | 2019 | 490 |
9,114 | Exploration via Hindsight Goal Generation Zhizhou Ren†, Kefan Dong† Institute for Interdisciplinary Information Sciences, Tsinghua University Department of Computer Science, University of Illinois at Urbana-Champaign {rzz16, dkf16}@mails.tsinghua.edu.cn Yuan Zhou Department of Industrial and Enterprise Syst... | 2019 | 491 |
9,115 | VIREL: A Variational Inference Framework for Reinforcement Learning Matthew Fellows∗Anuj Mahajan∗Tim G. J. Rudner Shimon Whiteson Department of Computer Science University of Oxford Abstract Applying probabilistic models to reinforcement learning (RL) enables the uses of powerful optimisation tools such... | 2019 | 492 |
9,116 | What Can ResNet Learn Efficiently, Going Beyond Kernels?∗ Zeyuan Allen-Zhu Microsoft Research AI zeyuan@csail.mit.edu Yuanzhi Li Carnegie Mellon University yuanzhil@andrew.cmu.edu Abstract How can neural networks such as ResNet efficiently learn CIFAR-10 with test accuracy more than 96%, while other m... | 2019 | 493 |
9,117 | Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration Clarice Poon∗ University of Bath, Bath UK cmhsp20@bath.ac.uk Jingwei Liang∗ University of Cambridge, Cambridge UK jl993@cam.ac.uk Abstract The alternating direction method of multipliers (ADMM) is one of the most widel... | 2019 | 494 |
9,118 | Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova⇤ Mila, Université de Montréal Idiap, École Polytechnique Fédérale de Lausanne Gauthier Gidel⇤ Mila, Université de Montréal Element AI François Fleuret Idiap, École Polytechnique Fédérale de Lausanne Simon Lacoste-J... | 2019 | 495 |
9,119 | Focused Quantization for Sparse CNNs Yiren Zhao∗1 Xitong Gao∗2 Daniel Bates1 Robert Mullins1 Cheng-Zhong Xu3 1 University of Cambridge 2 Shenzhen Institutes of Advanced Technology 3 University of Macau Abstract Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision... | 2019 | 496 |
9,120 | Submodular Function Minimization with Noisy Evaluation Oracle Shinji Ito∗ NEC Corporation, The University of Tokyo i-shinji@nec.com Abstract This paper considers submodular function minimization with noisy evaluation oracles that return the function value of a submodular objective with zero-mean additive no... | 2019 | 497 |
9,121 | Knowledge Extraction with No Observable Data Jaemin Yoo Seoul National University jaeminyoo@snu.ac.kr Minyong Cho Seoul National University chominyong@gmail.com Taebum Kim Seoul National University k.taebum@snu.ac.kr U Kang∗ Seoul National University ukang@snu.ac.kr Abstract Knowledge distil... | 2019 | 498 |
9,122 | Global Guarantees for Blind Demodulation with Generative Priors Paul Hand Dept. of Mathematics and College of Computer Science and Information Northeastern University, MA p.hand@northeastern.edu Babhru Joshi Dept. of Mathematics University of British Columbia, BC b.joshi@math.ubc.ca Abstract We st... | 2019 | 499 |
9,123 | Deep Equilibrium Models Shaojie Bai Carnegie Mellon University J. Zico Kolter Carnegie Mellon University Bosch Center for AI Vladlen Koltun Intel Labs Abstract We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers ... | 2019 | 5 |
9,124 | Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex Jielin Qiu1, Ge Huang2, Tai Sing Lee1,2 1Computer Science Department 2 Neuroscience Institute Carnegie Mellon University Pittsburgh, PA 15213 {jielinq,taislee}@andrew.cmu.edu Abstract In this paper we developed a co... | 2019 | 50 |
9,125 | Neural Jump Stochastic Differential Equations Junteng Jia Cornell University jj585@cornell.edu Austin R. Benson Cornell University arb@cs.cornell.edu Abstract Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic ev... | 2019 | 500 |
9,126 | Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning Nathan Kallus Cornell University New York, NY kallus@cornell.edu Masatoshi Uehara ∗ Harvard University Cambrdige, MA uehara_m@g.harvard.edu Abstract Off-policy evaluation (OPE) in both contextual bandits a... | 2019 | 501 |
9,127 | Learning about an exponential amount of conditional distributions Mohamed Ishmael Belghazi1,2 ishmael.belghazi@gmail.com Maxime Oquab1 qas@fb.com Yann Lecun1 yann@fb.com David Lopez-Paz1 dlp@fb.com 1Facebook AI Research, Paris, France 2Montréal Institute for Learning Algorithms, Montréal, Canada ... | 2019 | 502 |
9,128 | Multi-mapping Image-to-Image Translation via Learning Disentanglement Xiaoming Yu1,2, Yuanqi Chen1,2, Thomas Li1,3, Shan Liu4, and Ge Li 1,2 1School of Electronics and Computer Engineering, Peking University 2Peng Cheng Laboratory 3Advanced Institute of Information Technology, Peking University 4Tencent Am... | 2019 | 503 |
9,129 | Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization Miika Aittala MIT miika@csail.mit.edu Prafull Sharma MIT prafull@mit.edu Lukas Murmann MIT lmurmann@mit.edu Adam B. Yedidia MIT adamy@mit.edu Gregory W. Wornell MIT gww@mit.edu William T. Freeman MIT, G... | 2019 | 504 |
9,130 | Explicitly disentangling image content from translation and rotation with spatial-VAE Tristan Bepler Massachusetts Institute of Technology Cambridge, MA tbepler@mit.edu Ellen D. Zhong Massachusetts Institute of Technology Cambridge, MA zhonge@mit.edu Kotaro Kelley New York Structural Biology Cente... | 2019 | 505 |
9,131 | Imitation-Projected Programmatic Reinforcement Learning Abhinav Verma∗ Rice University averma@rice.edu Hoang M. Le∗ Caltech hmle@caltech.edu Yisong Yue Caltech yyue@caltech.edu Swarat Chaudhuri Rice University swarat@rice.edu Abstract We study the problem of programmatic reinforcement lear... | 2019 | 506 |
9,132 | The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies Ronen Basri1 David Jacobs2 Yoni Kasten1 Shira Kritchman1 1Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel 2Department of Computer Science, University of Maryland, College Park, MD Abstra... | 2019 | 507 |
9,133 | Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem Gonzalo Mena Harvard Jonathan Niles-Weed NYU Abstract We prove several fundamental statistical bounds for entropic OT with the squared Euclidean cost between subgaussian probability measures in arbitrary d... | 2019 | 508 |
9,134 | A Game Theoretic Approach to Class-wise Selective Rationalization Shiyu Chang1,2∗ Yang Zhang1,2∗ Mo Yu2∗ Tommi S. Jaakkola3 1MIT-IBM Watson AI Lab 2IBM Research 3CSAIL MIT {shiyu.chang,yang.zhang2}@ibm.com yum@us.ibm.com tommi@csail.mit.edu Abstract Selection of input features such as relevant... | 2019 | 509 |
9,135 | Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod Hebrew University yahav.bechavod@cs.huji.ac.il Katrina Ligett Hebrew University katrina@cs.huji.ac.il Aaron Roth University of Pennsylvania aaroth@cis.upenn.edu Bo Waggoner University of Colorado bwag@colorado.edu ... | 2019 | 51 |
9,136 | Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes Creighton Heaukulani No Affiliation Bangkok, Thailand c.k.heaukulani@gmail.com Mark van der Wilk PROWLER.io Cambridge, United Kingdom mark@prowler.io Abstract We implement gradient-based variational... | 2019 | 510 |
9,137 | Variational Bayesian Decision-making for Continuous Utilities Tomasz Ku´smierczyk Joseph Sakaya Arto Klami Helsinki Institute for Information Technology HIIT Department of Computer Science, University of Helsinki {tomasz.kusmierczyk,joseph.sakaya,arto.klami}@helsinki.fi Abstract Bayesian decision theo... | 2019 | 511 |
9,138 | Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation Zengfeng Huang School of Data Science Fudan University huangzf@fudan.edu.cn Ziyue Huang Department of CSE HKUST zhuangbq@cse.ust.hk Yilei Wang Department of CSE HKUST ywanggq@cse.ust.hk Ke Yi Department of CSE HKUST yike... | 2019 | 512 |
9,139 | Search on the Replay Buffer: Bridging Planning and Reinforcement Learning Benjamin Eysenbachθφ, Ruslan Salakhutdinovθ, Sergey Levineφψ θCMU, φGoogle Brain, ψUC Berkeley beysenba@cs.cmu.edu Abstract The history of learning for control has been an exciting back and forth between two broad classes of algorit... | 2019 | 513 |
9,140 | Minimal Variance Sampling in Stochastic Gradient Boosting Bulat Ibragimov Yandex, Moscow, Russia Moscow Institute of Physics and Technology ibrbulat@yandex.ru Gleb Gusev Sberbank∗, Moscow, Russia gusev.g.g@sberbank.ru Abstract Stochastic Gradient Boosting (SGB) is a widely used approach to regulariz... | 2019 | 514 |
9,141 | Transductive Zero-Shot Learning with Visual Structure Constraint Ziyu Wan∗1, Dongdong Chen∗2, Yan Li3, Xingguang Yan4 Junge Zhang5, Yizhou Yu6, Jing Liao†1 1 City University of Hong Kong 2 Microsoft Cloud+AI 3 PCG, Tencent 4 Shenzhen University 5 NLPR, CASIA 6 Deepwise AI Lab Abstract To recognize objects... | 2019 | 515 |
9,142 | Large Scale Markov Decision Processes with Changing Rewards Adrian Rivera Cardoso, He Wang School of Industrial and Systems Engineering Georgia Institute of Technology adrian.riv@gatech.edu, he.wang@isye.gatech.edu Huan Xu Alibaba Group huan.xu@alibaba-inc.com Abstract We consider Markov Decision Pr... | 2019 | 516 |
9,143 | 2019 | 517 | |
9,144 | Implicit Regularization for Optimal Sparse Recovery Tomas Vaškeviˇcius1, Varun Kanade2, Patrick Rebeschini1 1 Department of Statistics, 2 Department of Computer Science University of Oxford {tomas.vaskevicius, patrick.rebeschini}@stats.ox.ac.uk varunk@cs.ox.ac.uk Abstract We investigate implicit regulariz... | 2019 | 518 |
9,145 | Residual Flows for Invertible Generative Modeling Ricky T. Q. Chen1,3, Jens Behrmann2, David Duvenaud1,3, Jörn-Henrik Jacobsen1,3 University of Toronto1, University of Bremen2, Vector Institute3 rtqichen@cs.toronto.edu, jensb@uni-bremen.de duvenaud@cs.toronto.edu, j.jacobsen@vectorinstitute.ai Abstract Flow... | 2019 | 519 |
9,146 | Semi-Parametric Efficient Policy Learning with Continuous Actions Mert Demirer MIT mdemirer@mit.edu Vasilis Syrgkanis Microsoft Research vasy@microsoft.com Greg Lewis Microsoft Research glewis@microsoft.com Victor Chernozhukov MIT vchern@mit.edu Abstract We consider off-policy evaluation an... | 2019 | 52 |
9,147 | Copula Multi-label Learning Weiwei Liu School of Computer Science, Wuhan University Wuhan, China 430072 liuweiwei863@gmail.com Abstract A formidable challenge in multi-label learning is to model the interdependencies between labels and features. Unfortunately, the statistical properties of existing mult... | 2019 | 520 |
9,148 | Adversarial Training and Robustness for Multiple Perturbations Florian Tramèr Stanford University Dan Boneh Stanford University Abstract Defenses against adversarial examples, such as adversarial training, are typically tailored to a single perturbation type (e.g., small ℓ∞-noise). For other perturbatio... | 2019 | 521 |
9,149 | Certainty Equivalence is Efficient for Linear Quadratic Control Horia Mania University of California, Berkeley hmania@berkeley.edu Stephen Tu University of California, Berkeley stephentu@berkeley.edu Benjamin Recht University of California, Berkeley brecht@berkeley.edu Abstract We study the perfo... | 2019 | 522 |
9,150 | Stein Variational Gradient Descent with Matrix-Valued Kernels Dilin Wang* Ziyang Tang⇤Chandrajit Bajaj Qiang Liu Department of Computer Science, UT Austin {dilin, ztang, bajaj, lqiang}@cs.utexas.edu Abstract Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverag... | 2019 | 523 |
9,151 | Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate James Jordon University of Oxford james.jordon@wolfson.ox.ac.uk Jinsung Yoon University of California, Los Angeles jsyoon0823@g.ucla.edu Mihaela van der Schaar University of Cambridge University of Cali... | 2019 | 524 |
9,152 | Abstraction based Output Range Analysis for Neural Networks Pavithra Prabhakar∗, Zahra Rahimi Afzal∗ Department of Computer Science Kansas State University Manhattan, KS 66506 {pprabhakar,zrahimi}@ksu.edu Abstract In this paper, we consider the problem of output range analysis for feed-forward neura... | 2019 | 525 |
9,153 | Paraphrase Generation with Latent Bag of Words Yao Fu Department of Computer Science Columbia University yao.fu@columbia.edu Yansong Feng Institute of Computer Science and Technology Peking University fengyansong@pku.edu.cn John P. Cunningham Department of Statistics Columbia University jpc2181@... | 2019 | 526 |
9,154 | Combinatorial Bandits with Relative Feedback Aadirupa Saha Indian Institute of Science, Bangalore aadirupa@iisc.ac.in Aditya Gopalan Indian Institute of Science, Bangalore aditya@iisc.ac.in Abstract We consider combinatorial online learning with subset choices when only relative feedback information f... | 2019 | 527 |
9,155 | Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang∗ Microsoft Research AI gregyang@microsoft.com Abstract Wide neural networks with random weights and biases are Gaussian processes, as originally observed by Neal (1995) and more recently... | 2019 | 528 |
9,156 | An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums Hadrien Hendrikx INRIA - DIENS - PSL Research University hadrien.hendrikx@inria.fr Francis Bach INRIA - DIENS - PSL Research University francis.bach@inria.fr Laurent Massouli´e INRIA - DIENS - PSL Research University laurent.... | 2019 | 529 |
9,157 | Concentration of risk measures: A Wasserstein distance approach Sanjay P. Bhat Tata Consultancy Services Limited Hyderabad, Telangana, India sanjay.bhat@tcs.com Prashanth L.A. Department of Computer Science and Engineering Indian Institute of Technology Madras, India prashla@cse.iitm.ac.in ∗ Abstr... | 2019 | 53 |
9,158 | Sample Efficient Active Learning of Causal Trees Kristjan Greenewald IBM Research MIT-IBM Watson AI Lab kristjan.h.greenewald@ibm.com Dmitriy Katz IBM Research MIT-IBM Watson AI Lab dkatzrog@us.ibm.com Karthikeyan Shanmugam IBM Research MIT-IBM Watson AI Lab karthikeyan.shanmugam2@ibm.com Sara ... | 2019 | 530 |
9,159 | Data Cleansing for Models Trained with SGD Satoshi Hara⇤ Atsushi Nitanda† Takanori Maehara‡ Abstract Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models... | 2019 | 531 |
9,160 | Universality and individuality in neural dynamics across large populations of recurrent networks Niru Maheswaranathan∗ Google Brain, Google Inc. Mountain View, CA nirum@google.com Alex H. Williams∗ Stanford University Stanford, CA ahwillia@stanford.edu Matthew D. Golub Stanford University Stanfo... | 2019 | 532 |
9,161 | Generating Diverse High-Fidelity Images with VQ-VAE-2 Ali Razavi∗ DeepMind alirazavi@google.com Aäron van den Oord∗ DeepMind avdnoord@google.com Oriol Vinyals DeepMind vinyals@google.com Abstract We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale ima... | 2019 | 533 |
9,162 | When to Trust Your Model: Model-Based Policy Optimization Michael Janner Justin Fu Marvin Zhang Sergey Levine University of California, Berkeley {janner, justinjfu, marvin, svlevine}@eecs.berkeley.edu Abstract Designing effective model-based reinforcement learning algorithms is difficult because the ... | 2019 | 534 |
9,163 | On Making Stochastic Classifiers Deterministic Andrew Cotter, Harikrishna Narasimhan, Maya Gupta Google Research 1600 Amphitheatre Pkwy, Mountain View, CA 94043 {acotter,hnarasimhan,mayagupta}@google.com Abstract Stochastic classifiers arise in a number of machine learning problems, and have become especial... | 2019 | 535 |
9,164 | Blind Super-Resolution Kernel Estimation using an Internal-GAN SefiBell-Kligler Assaf Shocher Michal Irani Dept. of Computer Science and Applied Math The Weizmann Institute of Science, Israel Project website: http://www.wisdom.weizmann.ac.il/∼vision/kernelgan Abstract Super resolution (SR) methods typica... | 2019 | 536 |
9,165 | Learning to Learn via Self-Critique Antreas Antoniou University of Edinburgh {a.antoniou}@sms.ed.ac.uk Amos Storkey University of Edinburgh {a.storkey}@ed.ac.uk Abstract In few-shot learning, a machine learning system learns from a small set of labelled examples relating to a specific task, such that i... | 2019 | 537 |
9,166 | Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks Joshua Ka-Wing Lee Dept. EECS, MIT jk_lee@mit.edu Prasanna Sattigeri MIT-IBM Watson AI Lab, IBM Research psattig@us.ibm.com Gregory W. Wornell Dept. EECS, MIT gww@mit.edu Abstract The advent of deep learn... | 2019 | 538 |
9,167 | Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses Ulysse Marteau-Ferey INRIA - École Normale Supérieure PSL Reasearch University ulysse.marteau-ferey@inria.fr Francis Bach INRIA - École Normale Supérieure PSL Reasearch University francis.bach@inria.fr Alessand... | 2019 | 539 |
9,168 | Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem Dongdong Ge Research Institute for Interdisciplinary Sciences Shanghai University of Finance and Economics ge.dongdong@mail.shufe.edu.cn Haoyue Wang∗ School of Mathematical Sciences Fudan University haoyuewang14@fudan.edu.c... | 2019 | 54 |
9,169 | Is Deeper Better only when Shallow is Good? Eran Malach School of Computer Science The Hebrew University Jerusalem, Israel eran.malach@mail.huji.ac.il Shai Shalev-Shwartz School of Computer Science The Hebrew University Jerusalem, Israel shais@cs.huji.ac.il Abstract Understanding the power of de... | 2019 | 540 |
9,170 | Variance Reduced Policy Evaluation with Smooth Function Approximation Hoi-To Wai The Chinese University of Hong Kong Shatin, Hong Kong htwai@se.cuhk.edu.hk Mingyi Hong University of Minnesota Minneapolis, MN, USA mhong@umn.edu Zhuoran Yang Princeton University Princeton, NJ, USA zy6@princeton.... | 2019 | 541 |
9,171 | k-Means Clustering of Lines for Big Data Yair Marom Department of Computer Science University of Haifa Haifa, Israel yairmrm@gmail.com Dan Feldman Department of Computer Science University of Haifa Haifa, Israel dannyf.post@gmail.com Abstract The input to the k-mean for lines problem is a set L ... | 2019 | 542 |
9,172 | Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han Massachusetts Institute of Technology {ligeng, zhijian, songhan}@mit.edu Abstract Exchanging gradients is a widely used method in modern multi-node machine learning system (e.g., distributed training, collaborative learning). For a long time, ... | 2019 | 543 |
9,173 | Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi Department of Computer Engineering Sharif University of Technology Tehran, Iran najafy@ce.sharif.edu Shin-ichi Maeda Preferred Networks, Inc. Tokyo, Japan ichi@preferred.jp Masanori Koyama Preferred Networks, Inc... | 2019 | 544 |
9,174 | Pure Exploration with Multiple Correct Answers Rémy Degenne Centrum Wiskunde & Informatica Science Park 123, Amsterdam, NL remy.degenne@cwi.nl Wouter M. Koolen Centrum Wiskunde & Informatica Science Park 123, Amsterdam, NL wmkoolen@cwi.nl Abstract We determine the sample complexity of pure explorati... | 2019 | 545 |
9,175 | Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks∗ Gabriele Farina Computer Science Department Carnegie Mellon University gfarina@cs.cmu.edu Chun Kai Ling Computer Science Department Carnegie Mellon University chunkail@cs.cmu.edu Fei Fang Institute for Software Research ... | 2019 | 546 |
9,176 | The Thermodynamic Variational Objective Vaden Masrani1, Tuan Anh Le2, Frank Wood1 1Department of Computer Science, University of British Columbia 2Department of Brain and Cognitive Sciences, MIT Abstract We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete ... | 2019 | 547 |
9,177 | Sampling Sketches for Concave Sublinear Functions of Frequencies Edith Cohen Google Research, CA Tel Aviv University, Israel edith@cohenwang.com Ofir Geri Stanford University, CA ofirgeri@cs.stanford.edu Abstract We consider massive distributed datasets that consist of elements modeled as keyvalue pa... | 2019 | 548 |
9,178 | Solving Interpretable Kernel Dimension Reduction Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, and Jennifer Dy Electrical and Computer Engineering Dept., Northeastern University, Boston, MA Abstract Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data b... | 2019 | 549 |
9,179 | Coda: An End-to-End Neural Program Decompiler Cheng Fu, Huili Chen, Haolan Liu UC San Diego {cfu,huc044,hal022}@ucsd.edu Xinyun Chen UC Berkeley xinyun.chen@berkeley.edu Yuandong Tian Facebook yuandong@fb.com Farinaz Koushanfar, Jishen Zhao UC San Diego {farinaz,jzhao}@ucsd.edu Abstract Reve... | 2019 | 55 |
9,180 | Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao Stanford University kaidicao@stanford.edu Colin Wei Stanford University colinwei@stanford.edu Adrien Gaidon Toyota Research Institute adrien.gaidon@tri.global Nikos Arechiga Toyota Research Institute nikos.arechig... | 2019 | 550 |
9,181 | Multivariate Triangular Quantile Maps for Novelty Detection Jingjing Wang1, Sun Sun2, Yaoliang Yu1 University of Waterloo1, National Research Council Canada2 {jingjing.wang, sun.sun, yaoliang.yu}@uwaterloo.ca Abstract Novelty detection, a fundamental task in machine learning, has drawn a lot of recent att... | 2019 | 551 |
9,182 | Gradient-based Adaptive Markov Chain Monte Carlo Michalis K. Titsias DeepMind London, UK mtitsias@google.com Petros Dellaportas Department of Statistical Science University College of London, UK Department of Statistics, Athens Univ. of Econ. and Business, Greece and The Alan Turing Institute, UK ... | 2019 | 552 |
9,183 | Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers∗ Zeyuan Allen-Zhu Microsoft Research AI zeyuan@csail.mit.edu Yuanzhi Li Carnegie Mellon University yuanzhil@andrew.cmu.edu Yingyu Liang University of Wisconsin-Madison yliang@cs.wisc.edu Abstract The fundam... | 2019 | 553 |
9,184 | Online Forecasting of Total-Variation-bounded Sequences Dheeraj Baby Department of Computer Science UC Santa Barbara dheeraj@ucsb.edu Yu-Xiang Wang Department of Computer Science UC Santa Barbara yuxiangw@cs.ucsb.edu Abstract We consider the problem of online forecasting of sequences of length n w... | 2019 | 554 |
9,185 | Approximation Ratios of Graph Neural Networks for Combinatorial Problems Ryoma Sato1,2 Makoto Yamada1,2,3 Hisashi Kashima1,2 1Kyoto University 2RIKEN AIP 3JST PRESTO {r.sato@ml.ist.i, myamada@i, kashima@i}.kyoto-u.ac.jp Abstract In this paper, from a theoretical perspective, we study how powerful gr... | 2019 | 555 |
9,186 | Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video Jia-Wang Bian1,2, Zhichao Li3, Naiyan Wang3, Huangying Zhan1,2 Chunhua Shen1,2, Ming-Ming Cheng4, Ian Reid1,2 1University of Adelaide, Australia 2Australian Centre for Robotic Vision, Australia 3TuSimple, China 4Nankai Univer... | 2019 | 556 |
9,187 | Variational Denoising Network: Toward Blind Noise Modeling and Removal Zongsheng Yue1,2, Hongwei Yong2, Qian Zhao1, Lei Zhang2,3, Deyu Meng4,1,* 1 School of Mathematics and Statistics, Xi’an Jiaotong University, Shaanxi, China 2Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong 3DAM... | 2019 | 557 |
9,188 | Multi-task Learning for Aggregated Data using Gaussian Processes Fariba Yousefi Michael Thomas Smith Mauricio A. Álvarez Department of Computer Science, University of Sheffield {f.yousefi, m.t.smith, mauricio.alvarez}@sheffield.ac.uk Abstract Aggregated data is commonplace in areas such as epidemiology an... | 2019 | 558 |
9,189 | Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards Alexander Trott Salesforce Research atrott@salesforce.com Stephan Zheng Salesforce Research stephan.zheng@salesforce.com Caiming Xiong Salesforce Research cxiong@salesforce.com Richard Socher Salesforce Resear... | 2019 | 559 |
9,190 | GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen {huangyp,ylc,ankurbpn,orhanf,miachen,dehao hyouklee,jngiam,qvl,yonghui,zhifengc} @g... | 2019 | 56 |
9,191 | Efficient characterization of electrically evoked responses for neural interfaces Nishal P. Shah ∗ Stanford University Sasidhar Madugula Stanford University Pawel Hottowy AGH University of Science and Technology Alexander Sher University of California, Santa Cruz Alan Litke University of California... | 2019 | 560 |
9,192 | The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic Arash Ardakani, Zhengyun Ji, Amir Ardakani, Warren J. Gross Department of Electrical and Computer Engineering, McGill University, Montreal, Canada {arash.ardakani, zhengyun.ji, amir.ardakani}@mail.mcgill.ca warren.gross@mcgill.ca Abstract... | 2019 | 561 |
9,193 | HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models Sharon Zhou∗, Mitchell L. Gordon∗, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein Stanford University {sharonz, mgord, ranjaykrishna, aon2, feifeili, msb}@cs.stanford.edu Abstract Generative models often use huma... | 2019 | 562 |
9,194 | McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds Rui (Ray) Zhang ∗ School of Mathematics Monash University rui.zhang@monash.edu Xingwu Liu † Institute of Computing Technology, Chinese Academy of Sciences. University of Chinese Academy of Sciences liuxingwu@ict.ac.cn Y... | 2019 | 563 |
9,195 | Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices Santosh S. Vempala College of Computing Georgia Institute of Technology Atlanta, GA 30332 vempala@gatech.edu Andre Wibisono College of Computing Georgia Institute of Technology Atlanta, GA 30332 wibisono@gatech.edu Abst... | 2019 | 564 |
9,196 | Are sample means in multi-armed bandits positively or negatively biased? Jaehyeok Shin1, Aaditya Ramdas1,2 and Alessandro Rinaldo1 Department of Statistics and Data Science1 Machine Learning Department2 Carnegie Mellon University {shinjaehyeok, aramdas, arinaldo}@cmu.edu Abstract It is well known that i... | 2019 | 565 |
9,197 | The Landscape of Non-convex Empirical Risk with Degenerate Population Risk Shuang Li, Gongguo Tang, and Michael B. Wakin Department of Electrical Engineering Colorado School of Mines Golden, CO 80401 {shuangli,gtang,mwakin}@mines.edu Abstract The landscape of empirical risk has been widely studied in a ... | 2019 | 566 |
9,198 | Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks Xiao Sun Jungwook Choi∗ Chia-Yu Chen Naigang Wang Swagath Venkataramani Vijayalakshmi Srinivasan Xiaodong Cui Wei Zhang Kailash Gopalakrishnan IBM T. J. Watson Research Center Yorktown Heights, NY 10598, USA {xs... | 2019 | 567 |
9,199 | Are deep ResNets provably better than linear predictors? Chulhee Yun MIT Cambridge, MA 02139 chulheey@mit.edu Suvrit Sra MIT Cambridge, MA 02139 suvrit@mit.edu Ali Jadbabaie MIT Cambridge, MA 02139 jadbabai@mit.edu Abstract Recent results in the literature indicate that a residual network ... | 2019 | 568 |
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