NeurIPS 2018
1009 papers
3D-Aware Scene Manipulation via Inverse Graphics
Shunyu Yao, Tzu Ming Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, Bill Freeman, Josh Tenenbaum A Bayes-Sard Cubature Method
Toni Karvonen, Chris J Oates, Simo Sarkka A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon A Bridging Framework for Model Optimization and Deep Propagation
Risheng Liu, Shichao Cheng, Xiaokun Liu, Long Ma, Xin Fan, Zhongxuan Luo A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents
Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan A Dual Framework for Low-Rank Tensor Completion
Madhav Nimishakavi, Pratik Kumar Jawanpuria, Bamdev Mishra A Lyapunov-Based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh A No-Regret Generalization of Hierarchical SoftMax to Extreme Multi-Label Classification
Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski A Probabilistic Population Code Based on Neural Samples
Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj, Ralf Haefner A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger A Reduction for Efficient LDA Topic Reconstruction
Matteo Almanza, Flavio Chierichetti, Alessandro Panconesi, Andrea Vattani A Smoother Way to Train Structured Prediction Models
Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaid Harchaoui A Spectral View of Adversarially Robust Features
Shivam Garg, Vatsal Sharan, Brian Zhang, Gregory Valiant A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh, Baba Vemuri A Stein Variational Newton Method
Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, Robert Scheichl A Structured Prediction Approach for Label Ranking
Anna Korba, Alexandre Garcia, Florence d'Alché-Buc A Unified View of Piecewise Linear Neural Network Verification
Rudy R Bunel, Ilker Turkaslan, Philip Torr, Pushmeet Kohli, Pawan K Mudigonda A^2-Nets: Double Attention Networks
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng Active Matting
Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin Yin, Rynson Lau Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu Adversarial Examples That Fool Both Computer Vision and Time-Limited Humans
Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein Adversarial Multiple Source Domain Adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira, Geoffrey J. Gordon Adversarial Regularizers in Inverse Problems
Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb Adversarial Text Generation via Feature-Mover's Distance
Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry Adversarially Robust Optimization with Gaussian Processes
Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher Amortized Inference Regularization
Rui Shu, Hung H Bui, Shengjia Zhao, Mykel J Kochenderfer, Stefano Ermon An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski Approximation Algorithms for Stochastic Clustering
David Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas Pensyl Are GANs Created Equal? a Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap ATOMO: Communication-Efficient Learning via Atomic Sparsification
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, Stephen Wright Attention in Convolutional LSTM for Gesture Recognition
Liang Zhang, Guangming Zhu, Lin Mei, Peiyi Shen, Syed Afaq Ali Shah, Mohammed Bennamoun Banach Wasserstein GAN
Jonas Adler, Sebastian Lunz Bandit Learning in Concave N-Person Games
Mario Bravo, David Leslie, Panayotis Mertikopoulos Bandit Learning with Implicit Feedback
Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun Bayesian Model-Agnostic Meta-Learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn Bayesian Structure Learning by Recursive Bootstrap
Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon Bilevel Learning of the Group Lasso Structure
Jordan Frecon, Saverio Salzo, Massimiliano Pontil Bilinear Attention Networks
Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang Binary Rating Estimation with Graph Side Information
Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh BML: A High-Performance, Low-Cost Gradient Synchronization Algorithm for DML Training
Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang, Shuai Wang, Shu-Tao Xia, Jianping Wu Boosted Sparse and Low-Rank Tensor Regression
Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch BRUNO: A Deep Recurrent Model for Exchangeable Data
Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre Byzantine Stochastic Gradient Descent
Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li CatBoost: Unbiased Boosting with Categorical Features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh Chain of Reasoning for Visual Question Answering
Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong Co-Regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, Bill Freeman, Gregory Wornell Co-Teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama Communication Compression for Decentralized Training
Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu Compact Generalized Non-Local Network
Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu Compact Representation of Uncertainty in Clustering
Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum Conditional Adversarial Domain Adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I Jordan Connecting Optimization and Regularization Paths
Arun Suggala, Adarsh Prasad, Pradeep K Ravikumar Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt Context-Aware Synthesis and Placement of Object Instances
Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz Contextual Pricing for Lipschitz Buyers
Jieming Mao, Renato Leme, Jon Schneider Contextual Stochastic Block Models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel Contrastive Learning from Pairwise Measurements
Yi Chen, Zhuoran Yang, Yuchen Xie, Zhaoran Wang Coordinate Descent with Bandit Sampling
Farnood Salehi, Patrick Thiran, Elisa Celis Coupled Variational Bayes via Optimization Embedding
Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song cpSGD: Communication-Efficient and Differentially-Private Distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Brendan McMahan Data Center Cooling Using Model-Predictive Control
Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, Mk Ryu, Greg Imwalle Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, Angelia Nedich Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang Deep Dynamical Modeling and Control of Unsteady Fluid Flows
Jeremy Morton, Antony Jameson, Mykel J Kochenderfer, Freddie Witherden Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe Deep Generative Models with Learnable Knowledge Constraints
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Lianhui Qin, Xiaodan Liang, Haoye Dong, Eric P Xing Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu Deep Neural Nets with Interpolating Function as Output Activation
Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley Osher Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wangmeng Zuo, Wei Liu, Ming-Hsuan Yang Deep Poisson Gamma Dynamical Systems
Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou Deep State Space Models for Time Series Forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski Deepcode: Feedback Codes via Deep Learning
Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt Delta-Encoder: An Effective Sample Synthesis Method for Few-Shot Object Recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogerio Feris, Raja Giryes, Alex Bronstein Dialog-Based Interactive Image Retrieval
Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogerio Feris Differentiable MPC for End-to-End Planning and Control
Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots, J. Zico Kolter Differential Privacy for Growing Databases
Rachel Cummings, Sara Krehbiel, Kevin A Lai, Uthaipon Tantipongpipat Differentially Private Change-Point Detection
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang Diffusion Maps for Textual Network Embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin DifNet: Semantic Segmentation by Diffusion Networks
Peng Jiang, Fanglin Gu, Yunhai Wang, Changhe Tu, Baoquan Chen Direct Estimation of Differences in Causal Graphs
Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler Direct Runge-Kutta Discretization Achieves Acceleration
Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie Dirichlet-Based Gaussian Processes for Large-Scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone Discovery of Latent 3D Keypoints via End-to-End Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan J Tompson, Mohammad Norouzi Discrimination-Aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jinhui Zhu Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh, Dylan Nielson, Peter A Bandettini, Francisco Pereira Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij Domain-Invariant Projection Learning for Zero-Shot Recognition
An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen DropMax: Adaptive Variational SoftMax
Hae Beom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang Dual Policy Iteration
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Bagnell Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
Zhihui Zhu, Yifan Wang, Daniel Robinson, Daniel Naiman, René Vidal, Manolis Tsakiris Dual Swap Disentangling
Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, Dacheng Tao, Mingli Song Dynamic Network Model from Partial Observations
Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec E-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom Efficient Formal Safety Analysis of Neural Networks
Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana Efficient Inference for Time-Varying Behavior During Learning
Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W Pillow Efficient Nonmyopic Batch Active Search
Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett Embedding Logical Queries on Knowledge Graphs
Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec Empirical Risk Minimization Under Fairness Constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, Massimiliano Pontil End-to-End Differentiable Physics for Learning and Control
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter Entropy and Mutual Information in Models of Deep Neural Networks
Marylou Gabrié, Andre Manoel, Clément Luneau, Jean Barbier, Nicolas Macris, Florent Krzakala, Lenka Zdeborová Entropy Rate Estimation for Markov Chains with Large State Space
Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu Evolved Policy Gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, Pieter Abbeel Experimental Design for Cost-Aware Learning of Causal Graphs
Erik Lindgren, Murat Kocaoglu, Alexandros G Dimakis, Sriram Vishwanath Explanations Based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, Payel Das Exploration in Structured Reinforcement Learning
Jungseul Ok, Alexandre Proutiere, Damianos Tranos Exponentiated Strongly Rayleigh Distributions
Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka Extracting Relationships by Multi-Domain Matching
Yitong Li, Michael Murias, Geraldine Dawson, David E Carlson Factored Bandits
Julian Zimmert, Yevgeny Seldin Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh, Frank Wood Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis
Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent Fast Deep Reinforcement Learning Using Online Adjustments from the past
Steven Hansen, Alexander Pritzel, Pablo Sprechmann, Andre Barreto, Charles Blundell Fast Estimation of Causal Interactions Using Wold Processes
Flavio Figueiredo, Guilherme Resende Borges, Pedro O.S. Vaz de Melo, Renato Assunção Faster Neural Networks Straight from JPEG
Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski FD-GAN: Pose-Guided Feature Distilling GAN for Robust Person Re-Identification
Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, Hongsheng Li Flexible Neural Representation for Physics Prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F Fei-Fei, Josh Tenenbaum, Daniel L Yamins Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier FRAGE: Frequency-Agnostic Word Representation
Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu Fully Understanding the Hashing Trick
Casper B. Freksen, Lior Kamma, Kasper Green Larsen Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Deisenroth Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi Generalisation in Humans and Deep Neural Networks
Robert Geirhos, Carlos R. M. Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann Generalisation of Structural Knowledge in the Hippocampal-Entorhinal System
James Whittington, Timothy Muller, Shirely Mark, Caswell Barry, Tim Behrens Generalized Zero-Shot Learning with Deep Calibration Network
Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I Jordan Generalizing to Unseen Domains via Adversarial Data Augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese Geometrically Coupled Monte Carlo Sampling
Mark Rowland, Krzysztof M Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E Turner, Adrian Weller Geometry Based Data Generation
Ofir Lindenbaum, Jay Stanley, Guy Wolf, Smita Krishnaswamy GLoMo: Unsupervised Learning of Transferable Relational Graphs
Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G Wilson GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander Schwing, Murali Annavaram, Salman Avestimehr Graphical Generative Adversarial Networks
Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang Group Equivariant Capsule Networks
Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski Hamiltonian Variational Auto-Encoder
Anthony L Caterini, Arnaud Doucet, Dino Sejdinovic Hierarchical Graph Representation Learning with Differentiable Pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec HitNet: Hybrid Ternary Recurrent Neural Network
Peiqi Wang, Xinfeng Xie, Lei Deng, Guoqi Li, Dongsheng Wang, Yuan Xie HOGWILD!-Gibbs Can Be PanAccurate
Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti HOUDINI: Lifelong Learning as Program Synthesis
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri How Does Batch Normalization Help Optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry How Many Samples Are Needed to Estimate a Convolutional Neural Network?
Simon S Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Ross, Samuel J Gershman, Been Kim, Finale Doshi-Velez Hyperbolic Neural Networks
Octavian Ganea, Gary Becigneul, Thomas Hofmann Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead Implicit Reparameterization Gradients
Mikhail Figurnov, Shakir Mohamed, Andriy Mnih Improving Simple Models with Confidence Profiles
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen Inequity Aversion Improves Cooperation in Intertemporal Social Dilemmas
Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueñez-Guzman, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, Thore Graepel Inferring Networks from Random Walk-Based Node Similarities
Jeremy Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis Infinite-Horizon Gaussian Processes
Arno Solin, James Hensman, Richard E Turner Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez, Jeffrey Regier, Michael I Jordan, Nir Yosef Informative Features for Model Comparison
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton Invariant Representations Without Adversarial Training
Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg Is Q-Learning Provably Efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I Jordan Isolating Sources of Disentanglement in Variational Autoencoders
Ricky T. Q. Chen, Xuechen Li, Roger B Grosse, David K. Duvenaud KONG: Kernels for Ordered-Neighborhood Graphs
Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic Large Margin Deep Networks for Classification
Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth Latent Alignment and Variational Attention
Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander Rush Learning Abstract Options
Matthew Riemer, Miao Liu, Gerald Tesauro Learning and Testing Causal Models with Interventions
Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy Learning Attractor Dynamics for Generative Memory
Yan Wu, Gregory Wayne, Karol Gregor, Timothy Lillicrap Learning Beam Search Policies via Imitation Learning
Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon Learning Compressed Transforms with Low Displacement Rank
Anna Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Ré Learning Convex Polytopes with Margin
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch Learning from Discriminative Feature Feedback
Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato Learning in Games with Lossy Feedback
Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye Learning Invariances Using the Marginal Likelihood
Mark van der Wilk, Matthias Bauer, St John, James Hensman Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum Learning Loop Invariants for Program Verification
Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel Learning Safe Policies with Expert Guidance
Jessie Huang, Fa Wu, Doina Precup, Yang Cai Learning SMaLL Predictors
Vikas Garg, Ofer Dekel, Lin Xiao Learning Sparse Neural Networks via Sensitivity-Driven Regularization
Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini Learning Task Specifications from Demonstrations
Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K Ho, Sanjit Seshia Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song Learning to Decompose and Disentangle Representations for Video Prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, Juan Carlos Niebles Learning to Exploit Stability for 3D Scene Parsing
Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum Learning to Learn Around a Common Mean
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil Learning to Multitask
Yu Zhang, Ying Wei, Qiang Yang Learning to Navigate in Cities Without a mAP
Piotr Mirowski, Matt Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell Learning to Optimize Tensor Programs
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy Learning to Play with Intrinsically-Motivated, Self-Aware Agents
Nick Haber, Damian Mrowca, Stephanie Wang, Li F Fei-Fei, Daniel L Yamins Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, Jiajun Wu Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher Reale, Rebecca Russell, Louis Kim, Peter Chin Learning to Share and Hide Intentions Using Information Regularization
Dj Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David J Schwab Learning to Solve SMT Formulas
Mislav Balunovic, Pavol Bielik, Martin Vechev Learning to Teach with Dynamic Loss Functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu Learning Towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song Learning with SGD and Random Features
Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco Legendre Decomposition for Tensors
Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda Leveraged Volume Sampling for Linear Regression
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu LF-Net: Learning Local Features from Images
Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Alessandro Achille, Tom Eccles, Loic Matthey, Chris Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins Lifelong Inverse Reinforcement Learning
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