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8,500 | Unsupervised Learning of Object Keypoints for Perception and Control Tejas Kulkarni*1, Ankush Gupta*1, Catalin Ionescu1, Sebastian Borgeaud1, Malcolm Reynolds1, Andrew Zisserman1,2, and Volodymyr Mnih1 * indicates equal contribution 1DeepMind, London 2VGG, Department of Engineering Science, University of Ox... | 2019 | 1223 |
8,501 | G2SAT: Learning to Generate SAT Formulas Jiaxuan You1∗ jiaxuan@cs.stanford.edu Haoze Wu1∗ haozewu@stanford.edu Clark Barrett1 barrett@cs.stanford.edu Raghuram Ramanujan2 raramanujan@davidson.edu Jure Leskovec1 jure@cs.stanford.edu 1Department of Computer Science, Stanford University 2Department ... | 2019 | 1224 |
8,502 | The Functional Neural Process Christos Louizos University of Amsterdam TNO Intelligent Imaging c.louizos@uva.nl Xiahan Shi Bosch Center for Artificial Intelligence UvA-Bosch Delta Lab xiahan.shi@de.bosch.com Klamer Schutte TNO Intelligent Imaging klamer.schutter@tno.nl Max Welling University of... | 2019 | 1225 |
8,503 | Convergent Policy Optimization for Safe Reinforcement Learning Ming Yu ⇤ Zhuoran Yang † Mladen Kolar ‡ Zhaoran Wang § Abstract We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both t... | 2019 | 1226 |
8,504 | A Refined Margin Distribution Analysis for Forest Representation Learning Shen-Huan Lyu, Liang Yang, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing, 210023, China {lvsh,yangl,zhouzh}@lamda.nju.edu.cn Abstract In this paper, we formulate the forest representat... | 2019 | 1227 |
8,505 | Diffeomorphic Temporal Alignment Nets Ron Shapira Weber Ben-Gurion University ronsha@post.bgu.ac.il Matan Eyal Ben-Gurion University mataney@post.bgu.ac.il Nicki Skafte Detlefsen Technical University of Denmark nsde@dtu.dk Oren Shriki Ben-Gurion University shrikio@bgu.ac.il Oren Freifeld Ben... | 2019 | 1228 |
8,506 | Multi-source Domain Adaptation for Semantic Segmentation Sicheng Zhao1∗†, Bo Li23†, Xiangyu Yue1†, Yang Gu2, Pengfei Xu2, Runbo Hu2, Hua Chai2, Kurt Keutzer1 1University of California, Berkeley, USA 2Didi Chuxing, China 3Harbin Institute of Technology, China schzhao@gmail.com, drluodian@gmail.com, {xyyue,ke... | 2019 | 1229 |
8,507 | A Bayesian Theory of Conformity in Collective Decision Making Koosha Khalvati Paul G. Allen School of CSE University of Washington koosha@cs.washington.edu Saghar Mirbagheri Department of Psychology New York University sm7369@nyu.edu Seongmin A. Park Center for Mind and Brain University of Calif... | 2019 | 123 |
8,508 | Spectral Modification of Graphs for Improved Spectral Clustering Ioannis Koutis Department of Computer Science New Jersey Institute of Technology Newark, NJ 07102 ikoutis@njit.edu Huong Le Department of Computer Science New Jersey Institute of Technology Newark, NJ 07102 hyl4@njit.edu Abstract ... | 2019 | 1230 |
8,509 | On Exact Computation with an Infinitely Wide Neural Net⇤ Sanjeev Arora† Simon S. Du‡ Wei Hu§ Zhiyuan Li¶ Ruslan Salakhutdinovk Ruosong Wang⇤⇤ Abstract How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard dataset such as CIFAR-10 when its “width”— namely, number o... | 2019 | 1231 |
8,510 | Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity 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 We study finite sample expressivity, ... | 2019 | 1232 |
8,511 | Amortized Bethe Free Energy Minimization for Learning MRFs Sam Wiseman Toyota Technological Institute at Chicago Chicago, IL, USA swiseman@ttic.edu Yoon Kim Harvard University Cambridge, MA, USA yoonkim@seas.harvard.edu Abstract We propose to learn deep undirected graphical models (i.e., MRFs) wit... | 2019 | 1233 |
8,512 | Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence Fengxiang He Tongliang Liu Dacheng Tao UBTECH Sydney AI Centre, School of Computer Science, Faculty of Engineering The University of Sydney, Darlington, NSW 2008, Australia {fengxiang.he, tongliang.liu, dacheng.tao... | 2019 | 1234 |
8,513 | XLNet: Generalized Autoregressive Pretraining for Language Understanding Zhilin Yang∗1, Zihang Dai∗12, Yiming Yang1, Jaime Carbonell1, Ruslan Salakhutdinov1, Quoc V. Le2 1Carnegie Mellon University, 2Google AI Brain Team {zhiliny,dzihang,yiming,jgc,rsalakhu}@cs.cmu.edu, qvl@google.com Abstract With the ca... | 2019 | 1235 |
8,514 | Conditional Independence Testing using Generative Adversarial Networks Alexis Bellot1,2 Mihaela van der Schaar1,2,3 1University of Cambridge, 2The Alan Turing Institute, 3University of California Los Angeles [abellot,mschaar]@turing.ac.uk Abstract We consider the hypothesis testing problem of detecting co... | 2019 | 1236 |
8,515 | A Tensorized Transformer for Language Modeling Xindian Ma1, Peng Zhang1∗, Shuai Zhang1, Nan Duan2, Yuexian Hou1, Dawei Song3, Ming Zhou2 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2Microsoft Research Asia, Beijing, China 3School of Computer Science and Technology, Beijing Insti... | 2019 | 1237 |
8,516 | Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components Sascha Saralajew1,∗Lars Holdijk1,∗Maike Rees1 Ebubekir Asan1 Thomas Villmann2,∗ 1Dr. Ing. h.c. F. Porsche AG, Weissach, Germany, sascha.saralajew@porsche.de 2University of Applied Sciences Mittweida, Mittweida, Germany, ... | 2019 | 1238 |
8,517 | Recurrent Registration Neural Networks for Deformable Image Registration Robin Sandkühler Department of Biomedical Engineering University of Basel, Switzerland robin.sandkuehler@unibas.ch Simon Andermatt Department of Biomedical Engineering University of Basel, Switzerland simon.andermatt@unibas.ch ... | 2019 | 1239 |
8,518 | Poisson-Randomized Gamma Dynamical Systems Aaron Schein Data Science Institute Columbia University Scott W. Linderman Department of Statistics Stanford University Mingyuan Zhou McCombs School of Business University of Texas at Austin David M. Blei Department of Statistics Columbia University H... | 2019 | 124 |
8,519 | User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning Dirk van der Hoeven Mathematical Institute Leiden University Leiden, 2333 CA dirkvderhoeven@gmail.com Abstract Local differential privacy is a strong notion of privacy in which the provider of the data guarantees privac... | 2019 | 1240 |
8,520 | Learning Representations by Maximizing Mutual Information Across Views Philip Bachman Microsoft Research phil.bachman@gmail.com R Devon Hjelm Microsoft Research, MILA devon.hjelm@microsoft.com William Buchwalter Microsoft Research wibuch@microsoft.com Abstract We propose an approach to self-supe... | 2019 | 1241 |
8,521 | Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs Jian Qian, Ronan Fruit Sequel Team - Inria Lille jian.qian@ens.fr, ronan.fruit@inria.fr Matteo Pirotta, Alessandro Lazaric Facebook AI Research {pirotta, lazaric}@fb.com Abstract The exploration bonus is an effect... | 2019 | 1242 |
8,522 | A neurally plausible model learns successor representations in partially observable environments Eszter Vértes Maneesh Sahani Gatsby Computational Neuroscience Unit University College London London W1T 4JG, UK. {eszter,maneesh}@gatsby.ucl.ac.uk Abstract Animals need to devise strategies to maximize re... | 2019 | 1243 |
8,523 | Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD Phuong Ha Nguyen Electrical and Computer Engineering University of Connecticut, USA phuongha.ntu@gmail.com Lam M. Nguyen IBM Research, Thomas J. Watson Research Center Yorktown Heights, USA Lam... | 2019 | 1244 |
8,524 | Cost effective active search Shali Jiang CSE, WUSTL St. Louis, MO 63130 jiang.s@wustl.edu Benjamin Moseley Tepper School of Business, CMU and Relational AI Pittsburgh, PA 15213 moseleyb@andrew.cmu.edu Roman Garnett CSE, WUSTL St. Louis, MO 63130 garnett@wustl.edu Abstract We study a paradi... | 2019 | 1245 |
8,525 | Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up Dominic Richards Department of Statistics University of Oxford 24-29 St Giles’, Oxford, OX1 3LB dominic.richards@spc.ox.ac.uk Patrick Rebeschini Department of Statistics University of Oxford 24-29 St Giles’, O... | 2019 | 1246 |
8,526 | Modeling Conceptual Understanding in Image Reference Games Rodolfo Corona∗† UC Berkeley rcorona@berkeley.edu Stephan Alaniz∗† Max Planck Institute for Informatics salaniz@mpi-inf.mpg.de Zeynep Akata† University of Tübingen zeynep.akata@uni-tuebingen.de Abstract An agent who interacts with a wide... | 2019 | 1247 |
8,527 | Inherent Weight Normalization in Stochastic Neural Networks Georgios Detorakis Department of Cognitive Sciences University of California Irvine Irvine, CA 92697 gdetorak@uci.edu Sourav Dutta Department of Electrical Engineering University of Notre Dame Notre Dame, IN 46556 USA sdutta4@nd.edu Abh... | 2019 | 1248 |
8,528 | Universality in Learning from Linear Measurements Ehsan Abbasi Department of Electrical Engineering California Institute of Technology Pasadena, CA, 91125 eabbasi@caltech.edu Fariborz Salehi Department of Electrical Engineering California Institute of Technology Pasadena, CA, 91125 fsalehi@caltech.e... | 2019 | 1249 |
8,529 | Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran Google Brain Michael W. Dusenberry Google Brain∗ Mark van der Wilk Prowler.io Danijar Hafner Google Brain Abstract We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It exte... | 2019 | 125 |
8,530 | Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design Faidra Monachou Stanford University monachou@stanford.edu Itai Ashlagi Stanford University iashlagi@stanford.edu Abstract The increasing popularity of online two-sided markets such as ride-sharing, acco... | 2019 | 1250 |
8,531 | Structure Learning with Side Information: Sample Complexity Saurabh Sihag Ali Tajer Electrical, Computer, and Systems Engineering Department Rensselaer Polytechnic Institute Abstract Graphical models encode the stochastic dependencies among random variables (RVs). The vertices represent the RVs, and the e... | 2019 | 1251 |
8,532 | Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran1 Keyon Vafa12∗Kumar Krishna Agrawal1† Laurent Dinh1 Ben Poole1 1Google Brain 2Columbia University Abstract While normalizing flows have led to significant advances in modeling highdimensional continuous distributions, their applicab... | 2019 | 1252 |
8,533 | Disentangled behavioral representations Amir Dezfouli1∗Hassan Ashtiani2 Omar Ghattas13 Richard Nock145 Peter Dayan6# Cheng Soon Ong14 1Data61, CSIRO 2McMaster University 3University of Chicago 4Australian National University 5University of Sydney 6Max Planck Institute Corresponding authors ∗amir.dezfouli@data... | 2019 | 1253 |
8,534 | A Flexible Generative Framework for Graph-based Semi-supervised Learning Jiaqi Ma∗† jiaqima@umich.edu Weijing Tang∗‡ weijtang@umich.edu Ji Zhu‡ jizhu@umich.edu Qiaozhu Mei†§ qmei@umich.edu Abstract We consider a family of problems that are concerned about making predictions for the majority of u... | 2019 | 1254 |
8,535 | A New Perspective on Pool-Based Active Classification and False-Discovery Control Lalit Jain, Kevin Jamieson {lalitj, jamieson}@cs.washington.edu Paul G. Allen School of Computer Science & Engineering University of Washington, Seattle, WA Abstract In many scientific settings there is a need for adaptive exp... | 2019 | 1255 |
8,536 | Online-Within-Online Meta-Learning Giulia Denevi1,2, Dimitris Stamos3, Carlo Ciliberto3,4 and Massimiliano Pontil1,3 1Istituto Italiano di Tecnologia (Italy), 2University of Genoa (Italy), 3University College of London (UK),4Imperial College of London (UK), giulia.denevi@iit.it, c.ciliberto@imperial.ac.uk, {d.s... | 2019 | 1256 |
8,537 | Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model Guodong Zhang1,2,3⇤, Lala Li3, Zachary Nado3, James Martens4, Sushant Sachdeva1, George E. Dahl3, Christopher J. Shallue3, Roger Grosse1,2 1University of Toronto, 2Vector Institute, 3Google Research, Brain Team, 4DeepMi... | 2019 | 1257 |
8,538 | Using Statistics to Automate Stochastic Optimization Hunter Lang Pengchuan Zhang Lin Xiao Microsoft Research AI Redmond, WA 98052, USA {hunter.lang, penzhan, lin.xiao}@microsoft.com Abstract Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient method... | 2019 | 1258 |
8,539 | Margin-Based Generalization Lower Bounds for Boosted Classifiers Allan Grønlund ‡§ Lior Kamma § Kasper Green Larsen § Alexander Mathiasen § Jelani Nelson ∗ Abstract Boosting is one of the most successful ideas in machine learning. The most wellaccepted explanations for the low generalization error of boo... | 2019 | 1259 |
8,540 | Sequence Modeling with Unconstrained Generation Order Dmitrii Emelianenko1,2 Elena Voita1,3 Pavel Serdyukov1 1Yandex, Russia 2National Research University Higher School of Economics, Russia 3University of Amsterdam, Netherlands {dimdi-y, lena-voita, pavser}@yandex-team.ru Abstract The dominant appro... | 2019 | 126 |
8,541 | D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen Department of Computer Science and Engineering Washington University in St. Louis {muhan, jiang.s, z.cui, garnett}@wustl.edu, chen@cse.wustl.edu Abstract Graph structured data are... | 2019 | 1260 |
8,542 | Adversarial Examples are not Bugs, they are Features Andrew Ilyas∗ MIT ailyas@mit.edu Shibani Santurkar∗ MIT shibani@mit.edu Dimitris Tsipras∗ MIT tsipras@mit.edu Logan Engstrom∗ MIT engstrom@mit.edu Brandon Tran MIT btran115@mit.edu Aleksander M ˛adry MIT madry@mit.edu Abstract ... | 2019 | 1261 |
8,543 | Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory Bin Hu, Usman Ahmed Syed Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Abstract In this paper, we provide a... | 2019 | 1262 |
8,544 | Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion Yiqi Zhong∗ University of Southern California Los Angeles, California yiqizhon@usc.edu Cho-Ying Wu∗ University of Southern California Los Angeles, California choyingw@usc.edu Suya You US Army Research Laboratory Playa Vista, C... | 2019 | 1263 |
8,545 | Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck Maximilian Igl ⇤ University of Oxford Kamil Ciosek Microsoft Research Yingzhen Li Microsoft Research Sebastian Tschiatschek Microsoft Research Cheng Zhang Microsoft Research Sam Devlin † Microsof... | 2019 | 1264 |
8,546 | Untangling in Invariant Speech Recognition Cory Stephenson Intel AI Lab cory.stephenson@intel.com Jenelle Feather MIT jfeather@mit.edu Suchismita Padhy Intel AI Lab suchismita.padhy@intel.com Oguz Elibol Intel AI Lab oguz.h.elibol@intel.com Hanlin Tang Intel AI Lab hanlin.tang@intel.com ... | 2019 | 1265 |
8,547 | Fast structure learning with modular regularization Greg Ver Steeg Information Sciences Institute University of Southern California Marina del Rey, CA 90292 gregv@isi.edu Hrayr Harutyunyan Information Sciences Institute University of Southern California Marina del Rey, CA 90292 hrayrh@isi.edu Dani... | 2019 | 1266 |
8,548 | Graph-based Discriminators: Sample Complexity and Expressiveness Roi Livni Tel Aviv University rlivni@tauex.tau.ac.il Yishay Mansour Tel Aviv University and Google mansour.yishay@gmail.com Abstract A basic question in learning theory is to identify if two distributions are identical when we have acc... | 2019 | 1267 |
8,549 | Certifiable Robustness to Graph Perturbations Aleksandar Bojchevski Technical University of Munich a.bojchevski@in.tum.de Stephan Günnemann Technical University of Munich guennemann@in.tum.de Abstract Despite the exploding interest in graph neural networks there has been little effort to verify and imp... | 2019 | 1268 |
8,550 | Surfing: Iterative Optimization Over Incrementally Trained Deep Networks Ganlin Song Department of Statistics and Data Science Yale University ganlin.song@yale.edu Zhou Fan Department of Statistics and Data Science Yale University zhou.fan@yale.edu John Lafferty Department of Statistics and Data Sc... | 2019 | 1269 |
8,551 | Online Continual Learning with Maximally Interfered Retrieval Rahaf Aljundi∗ KU Leuven rahaf.aljundi@gmail.com Lucas Caccia∗ Mila lucas.page-caccia@mail.mcgill.ca Eugene Belilovsky∗ Mila eugene.belilovsky@umontreal.ca Massimo Caccia∗ Mila massimo.p.caccia@gmail.com Min Lin Mila mavenlin@... | 2019 | 127 |
8,552 | Rates of Convergence for Large-scale Nearest Neighbor Classification Xingye Qiao Department of Mathematical Sciences Binghamton University New York, USA qiao@math.binghamton.edu Jiexin Duan Department of Statistics Purdue University West Lafayette, Indiana, USA duan32@purdue.edu Guang Cheng Dep... | 2019 | 1270 |
8,553 | Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning Harsh Gupta ECE and CSL University of Illinois at Urbana-Champaign hgupta10@illinois.edu R. Srikant ECE and CSL University of Illinois at Urbana-Champaign rsrikant@illinois.edu Lei Ying ... | 2019 | 1271 |
8,554 | Pseudo-Extended Markov Chain Monte Carlo Christopher Nemeth Department of Mathematics and Statistics Lancaster University United Kingdom c.nemeth@lancaster.ac.uk Fredrik Lindsten Department of Computer and Information Science Linköping University Sweden fredrik.lindsten@liu.se Maurizio Filippone ... | 2019 | 1272 |
8,555 | Hierarchical Optimal Transport for Multimodal Distribution Alignment John Lee†⇤, Max Dabagia†, Eva L. Dyer†‡§, Christopher J. Rozell†§ †School of Electrical and Computer Engineering, ‡Coulter Department of Biomedical Engineering Georgia Institute of Technology, Atlanta, GA, 30332 USA {john.lee, maxdabagia, ... | 2019 | 1273 |
8,556 | Sampled Softmax with Random Fourier Features Ankit Singh Rawat, Jiecao Chen, Felix Yu, Ananda Theertha Suresh, and Sanjiv Kumar Google Research, New York {ankitsrawat, chenjiecao, felixyu, theertha, sanjivk}@google.com Abstract The computational cost of training with softmax cross entropy loss grows linearly ... | 2019 | 1274 |
8,557 | ϵ-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits Sivan Sabato Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva, Israel 8410501 sabatos@cs.bgu.ac.il Abstract We study ϵ-best-arm identification, in a setting where during the exploration phase, the cost of each ar... | 2019 | 1275 |
8,558 | On Differentially Private Graph Sparsification and Applications Raman Arora Johns Hopkins University arora@cs.jhu.edu Jalaj Upadhyay Rutgers University jalaj.kumar.upadhyay@gmail.com Abstract In this paper, we study private sparsification of graphs. In particular, we give an algorithm that given an in... | 2019 | 1276 |
8,559 | On the number of variables to use in principal component regression Ji Xu Columbia University jixu@cs.columbia.edu Daniel Hsu Columbia University djhsu@cs.columbia.edu Abstract We study least squares linear regression over N uncorrelated Gaussian features that are selected in order of decreasing var... | 2019 | 1277 |
8,560 | Self-Routing Capsule Networks Taeyoung Hahn Myeongjang Pyeon Gunhee Kim Seoul National University, Seoul, Korea {taeyounghahn,mjpyeon,gunhee}@snu.ac.kr http://vision.snu.ac.kr/projects/self-routing Abstract Capsule networks have recently gained a great deal of interest as a new architecture of neural ... | 2019 | 1278 |
8,561 | Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation Xueying Bai∗‡, Jian Guan∗§, Hongning Wang† ‡Department of Computer Science, Stony Brook University § Department of Computer Science and Technology, Tsinghua University † Department of Computer Science, University of Virgini... | 2019 | 1279 |
8,562 | Visualizing and Measuring the Geometry of BERT Andy Coenen⇤, Emily Reif⇤, Ann Yuan⇤ Been Kim, Adam Pearce, Fernanda Viégas, Martin Wattenberg Google Brain Cambridge, MA {andycoenen,ereif,annyuan,beenkim,adampearce,viegas,wattenberg}@google.com Abstract Transformer architectures show significant promise for... | 2019 | 128 |
8,563 | Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation Risto Vuorio∗1 Shao-Hua Sun∗2 Hexiang Hu2 Joseph J. Lim2 1University of Michigan 2University of Southern California vuoristo@gmail.com {shaohuas, hexiangh, limjj}@usc.edu Abstract Model-agnostic meta-learners aim to acquire meta-lea... | 2019 | 1280 |
8,564 | Predicting the Politics of an Image Using Webly Supervised Data Christopher Thomas Adriana Kovashka Department of Computer Science University of Pittsburgh Pittsburgh, PA 15213 {chris,kovashka}@cs.pitt.edu Abstract The news media shape public opinion, and often, the visual bias they contain is evide... | 2019 | 1281 |
8,565 | On the Curved Geometry of Accelerated Optimization Aaron Defazio Facebook AI Research New York Abstract In this work we propose a differential geometric motivation for Nesterov’s accelerated gradient method (AGM) for strongly-convex problems. By considering the optimization procedure as occurring on a Rie... | 2019 | 1282 |
8,566 | How to Initialize your Network? Robust Initialization for WeightNorm & ResNets Devansh Arpit∗†, Víctor Campos∗‡, Yoshua Bengio§ †Salesforce Research, ‡Barcelona Supercomputing Center, §Montréal Institute for Learning Algorithms, Université de Montréal, CIFAR Senior Fellow devansharpit@gmail.com, victor.campos... | 2019 | 1283 |
8,567 | Code Generation as a Dual Task of Code Summarization Bolin Wei†, Ge Li†∗, Xin Xia‡, Zhiyi Fu†, Zhi Jin†∗ †Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education, China; Software Institute, Peking University, China ‡Faculty of Information Technology, Monash University,... | 2019 | 1284 |
8,568 | A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation Mitsuru Kusumoto ∗ Preferred Networks, Inc. mkusumoto@preferred.jp Takuya Inoue ∗ The University of Tokyo inoue-takuya57@g.ecc.u-tokyo.ac.jp Gentaro Watanabe Preferred Networks, Inc. g.wtnb@preferred.jp Tak... | 2019 | 1285 |
8,569 | Gradient based sample selection for online continual learning Rahaf Aljundi∗ KU Leuven rahaf.aljundi@gmail.com Min Lin Mila mavenlin@gmail.com Baptiste Goujaud Mila baptiste.goujaud@gmail.com Yoshua Bengio Mila yoshua.bengio@mila.quebec Abstract A continual learning agent learns online wit... | 2019 | 1286 |
8,570 | Conditional Structure Generation through Graph Variational Generative Adversarial Nets Carl Yang∗, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li University of Illinois at Urbana Champaign, Urbana, IL 61801 {jiyang3, peiye, wenhans2, alanluu2, panli2}@illinois.edu Abstract Graph embedding has been intensively s... | 2019 | 1287 |
8,571 | Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu1, Qi Xie1, Lixuan Yi1, Qian Zhao1, Sanping Zhou1, Zongben Xu1, and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural ... | 2019 | 1288 |
8,572 | Thompson Sampling with Information Relaxation Penalties Seungki Min Columbia Business School Costis Maglaras Columbia Business School Ciamac C. Moallemi Columbia Business School Abstract We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an infor... | 2019 | 1289 |
8,573 | Learning to Predict Without Looking Ahead: World Models Without Forward Prediction C. Daniel Freeman, Luke Metz, David Ha Google Brain {cdfreeman, lmetz, hadavid}@google.com Abstract Much of model-based reinforcement learning involves learning a model of an agent’s world, and training an agent to leverage... | 2019 | 129 |
8,574 | Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback∗ Shinji Ito† NEC Corporation, The University of Tokyo i-shinji@nec.com Daisuke Hatano RIKEN AIP daisuke.hatano@riken.jp Hanna Sumita Tokyo Metropolitan University sumita@tmu.ac.jp Kei Takemura NEC Corporation kei_ta... | 2019 | 1290 |
8,575 | Constraint-based Causal Structure Learning with Consistent Separating Sets Honghao Li, Vincent Cabeli, Nadir Sella, Hervé Isambert∗ Institut Curie, PSL Research University, CNRS UMR168, Paris {honghao.li, vincent.cabeli, nadir.sella, herve.isambert}@curie.fr Abstract We consider constraint-based methods for... | 2019 | 1291 |
8,576 | Efficient Deep Approximation of GMMs Shirin Jalali, Carl Nuzman, Iraj Saniee Bell Labs, Nokia 600-700 Mountain Avenue Murray Hill, NJ 07974 {shirin.jalali,carl.nuzman,iraj.saniee}@nokia-bell-labs.com Abstract The universal approximation theorem states that any regular function can be approximated closely u... | 2019 | 1292 |
8,577 | Selecting causal brain features with a single conditional independence test per feature Atalanti A. Mastakouri Empirical Inference Department Max Planck Institute for Intelligent Systems Tübingen, 72076 amastakouri@tue.mpg.de Bernhard Schölkopf Empirical Inference Department Max Planck Institute f... | 2019 | 1293 |
8,578 | Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update Su Young Lee, Sungik Choi, Sae-Young Chung School of Electrical Engineering, KAIST, Republic of Korea {suyoung.l, si_choi, schung}@kaist.ac.kr Abstract We propose Episodic Backward Update (EBU) – a novel deep reinforcement learning... | 2019 | 1294 |
8,579 | Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior Cheng-Chun Hsu1∗ Kuang-Jui Hsu2∗ Chung-Chi Tsai2 Yen-Yu Lin1,3 Yung-Yu Chuang1,4 1Academia Sinica 2Qualcomm Technologies, Inc. 3National Chiao Tung University 4National Taiwan University hsu06118@citi.sinica.edu.tw {k... | 2019 | 1295 |
8,580 | Copula-like Variational Inference Marcel Hirt Department of Statistical Science University College of London, UK marcel.hirt.16@ucl.ac.uk Petros Dellaportas Department of Statistical Science University College of London, UK Department of Statistics Athens University of Economics and Business, Greece ... | 2019 | 1296 |
8,581 | Towards Hardware-Aware Tractable Learning of Probabilistic Models Laura I. Galindez Olascoaga†, Wannes Meert‡ , Nimish Shah† Marian Verhelst† and Guy Van den Broeck†† † Electrical Engineering Department, KU Leuven ‡ Computer Science Department, KU Leuven †† Computer Science Department, University of Califor... | 2019 | 1297 |
8,582 | Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples Tengyu Xu Department of Electrical and Computer Engineering The Ohio State University xu.3260@osu.edu Shaofeng Zou Department of Electrical Engineering University at Buffalo, The State University of New York szou3@bu... | 2019 | 1298 |
8,583 | Incremental Few-Shot Learning with Attention Attractor Networks Mengye Ren1,2,3, Renjie Liao1,2,3, Ethan Fetaya1,2, Richard S. Zemel1,2 1University of Toronto, 2Vector Institute, 3Uber ATG {mren, rjliao, ethanf, zemel}@cs.toronto.edu Abstract Machine learning classifiers are often trained to recognize a set ... | 2019 | 1299 |
8,584 | A Direct ˜O(1/ϵ) Iteration Parallel Algorithm for Optimal Transport Arun Jambulapati, Aaron Sidford, and Kevin Tian Stanford University {jmblpati,sidford,kjtian}@stanford.edu Abstract Optimal transportation, or computing the Wasserstein or “earth mover’s” distance between two n-dimensional distributions, ... | 2019 | 13 |
8,585 | Deep Generalized Method of Moments for Instrumental Variable Analysis Andrew Bennett⇤ Cornell University awb222@cornell.edu Nathan Kallus⇤ Cornell University kallus@cornell.edu Tobias Schnabel⇤ Microsoft Research tbs49@cornell.edu Abstract Instrumental variable analysis is a powerful tool for es... | 2019 | 130 |
8,586 | Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations Kevin A. Smith1,2,∗, Lingjie Mei3,∗, Shunyu Yao4,∗, Jiajun Wu3 Elizabeth Spelke2,5, Joshua B. Tenenbaum1,2,3, Tomer D. Ullman2,5 1 MIT BCS, 2 Center for Brains, Minds, & Machines, 3 MIT CSAIL, 4 Princeton Univ... | 2019 | 1300 |
8,587 | Outlier Detection and Robust PCA Using a Convex Measure of Innovation Mostafa Rahmani and Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue, WA 98004, USA {mostafarahmani,liping11}@baidu.com Abstract This paper presents a provable and strong algorithm, termed Innovation Search (... | 2019 | 1301 |
8,588 | Efficient Convex Relaxations for Streaming PCA Raman Arora Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 arora@cs.jhu.edu Teodor V. Marinov Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 tmarino2@jhu.edu Abstract We revisit two algorithms, matrix s... | 2019 | 1302 |
8,589 | Envy-Free Classification Maria-Florina Balcan Machine Learning Department Carnegie Mellon University ninamf@cs.cmu.edu Travis Dick Computer Science Department Carnegie Mellon University tdick@cs.cmu.edu Ritesh Noothigattu Machine Learning Department Carnegie Mellon University riteshn@cmu.edu Ar... | 2019 | 1303 |
8,590 | Deep Model Transferability from Attribution Maps Jie Song1,3, Yixin Chen1, Xinchao Wang2, Chengchao Shen1, Mingli Song1,3 1Zhejiang University, 2Stevens Institute of Technology 3Alibaba-Zhejiang University Joint Institute of Frontier Technologies {sjie,chenyix,chengchaoshen,brooksong}@zju.edu.cn xinchao.wang@... | 2019 | 1304 |
8,591 | Towards Interpretable Reinforcement Learning Using Attention Augmented Agents Alex Mott* Daniel Zoran* Mike Chrzanowski Daan Wierstra Danilo J. Rezende DeepMind London, UK {alexmott,danielzoran,chrzanowski,wierstra,danilor}@google.com Abstract Inspired by recent work in attention models for image ... | 2019 | 1305 |
8,592 | Weight Agnostic Neural Networks Adam Gaier† Bonn-Rhein-Sieg University of Applied Sciences Inria / CNRS / Université de Lorraine adam.gaier@h-brs.de David Ha Google Brain Tokyo, Japan hadavid@google.com Abstract Not all neural network architectures are created equal, some perform much better than ... | 2019 | 1306 |
8,593 | DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging Matthieu Simeoni ∗ IBM Zurich Research Laboratory meo@zurich.ibm.com Sepand Kashani † École Polytechnique Fédérale de Lausanne (EPFL) sepand.kashani@epfl.ch Paul Hurley Western Sydney University paul.hurley@westernsydney.edu.au Ma... | 2019 | 1307 |
8,594 | A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization Sulaiman A. Alghunaim, Kun Yuan Electrical and Computer Engineering Department University of California Los Angeles Los Angeles, CA, 90095 {salghunaim,kunyuan}@ucla.edu Ali H. Sayed Ecole Polytechnique Fédérale de Lausanne ... | 2019 | 1308 |
8,595 | Meta Architecture Search Albert Shaw1∗Wei Wei2 Weiyang Liu1 Le Song1,3 Bo Dai1,2 1Georgia Institute of Technology 2Google Research 3Ant Financial Abstract Neural Architecture Search (NAS) has been quite successful in constructing stateof-the-art models on a variety of tasks. Unfortunately, the computa... | 2019 | 1309 |
8,596 | Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders Natasa Tagasovska Department of Information Systems HEC Lausanne, Switzerland natasa.tagasovska@unil.ch Damien Ackerer Swissquote Bank Gland, Switzerland damien.ackerer@swissquote.ch Thibault Vatter Department of Statistics ... | 2019 | 131 |
8,597 | Double Quantization for Communication-Efficient Distributed Optimization Yue Yu IIIS, Tsinghua University yu-y14@mails.tsinghua.edu.cn Jiaxiang Wu Tencent AI Lab jonathanwu@tencent.com Longbo Huang∗ IIIS, Tsinghua University longbohuang@tsinghua.edu.cn Abstract Modern distributed training of mach... | 2019 | 1310 |
8,598 | Graph Structured Prediction Energy Networks Colin Graber Alexander Schwing cgraber2@illinois.edu aschwing@illinois.edu Department of Computer Science University of Illinois at Urbana-Champaign Champaign, IL Abstract For joint inference over multiple variables, a variety of structured prediction techni... | 2019 | 1311 |
8,599 | Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven École Normale Supérieure Paris, France nicolas.keriven@ens.fr Gabriel Peyré CNRS and École Normale Supérieure Paris, France gabriel.peyre@ens.fr Abstract Graph Neural Networks (GNN) come in many flavors, but should always be ... | 2019 | 1312 |
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