ICLR 2019

502 papers

Learning to Make Analogies by Contrasting Abstract Relational Structure

Felix Hill, Adam Santoro, David Barrett, Ari Morcos, Timothy Lillicrap

Towards Understanding Regularization in Batch Normalization

Ping Luo, Xinjiang Wang, Wenqi Shao, zhanglin peng

GamePad: A Learning Environment for Theorem Proving

Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever

Improving Generalization and Stability of Generative Adversarial Networks

Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh

The Singular Values of Convolutional Layers

Hanie Sedghi, Vineet Gupta, Philip M. Long

ACCELERATING NONCONVEX LEARNING VIA REPLICA EXCHANGE LANGEVIN DIFFUSION

Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang

Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards

Daniel McDuff, Ashish Kapoor

Small nonlinearities in activation functions create bad local minima in neural networks

Chulhee Yun, Suvrit Sra, Ali Jadbabaie

ADef: an Iterative Algorithm to Construct Adversarial Deformations

Rima Alaifari, Giovanni S. Alberti, Tandri Gauksson

code2seq: Generating Sequences from Structured Representations of Code

Uri Alon, Shaked Brody, Omer Levy, Eran Yahav

Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications

Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu

Relaxed Quantization for Discretized Neural Networks

Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling

Sparse Dictionary Learning by Dynamical Neural Networks

Tsung-Han Lin, Ping Tak Peter Tang

PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks

Jan Svoboda, Jonathan Masci, Federico Monti, Michael Bronstein, Leonidas Guibas

Whitening and Coloring Batch Transform for GANs

Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan

Deep reinforcement learning with relational inductive biases

Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Dan Hendrycks, Thomas Dietterich

GO Gradient for Expectation-Based Objectives

Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters

Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato

PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

James Jordon, Jinsung Yoon, Mihaela van der Schaar

Auxiliary Variational MCMC

Raza Habib, David Barber

KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks

James Jordon, Jinsung Yoon, Mihaela van der Schaar

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data

Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama

Approximability of Discriminators Implies Diversity in GANs

Yu Bai, Tengyu Ma, Andrej Risteski

Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers

Yonatan Geifman, Guy Uziel, Ran El-Yaniv

SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY

Namhoon Lee, Thalaiyasingam Ajanthan, Philip Torr

Deep Graph Infomax

Petar Veličković, William Fedus, William L. Hamilton, Pietro Lió, Yoshua Bengio

Neural Speed Reading with Structural-Jump-LSTM

Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma

Information Theoretic lower bounds on negative log likelihood

Luis A. Lastras-Montaño

Equi-normalization of Neural Networks

Pierre Stock, Benjamin Graham, Remi Gribonval, Herve Jegou

LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators

Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, Tingfa Xu

SGD Converges to Global Minimum in Deep Learning via Star-convex Path

Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh

CEM-RL: Combining evolutionary and gradient-based methods for policy search

Pourchot, Sigaud

Learning from Positive and Unlabeled Data with a Selection Bias

Masahiro Kato, Takeshi Teshima, Junya Honda

Multi-Domain Adversarial Learning

Alice Schoenauer-Sebag, Louise Heinrich, Marc Schoenauer, Michele Sebag, Lani F. Wu

Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation

Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao

Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models

Huan Zhang, Hai Zhao

Learning to Schedule Communication in Multi-agent Reinforcement Learning

Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee

Aggregated Momentum: Stability Through Passive Damping

James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse

Learning Factorized Multimodal Representations

Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, Ruslan Salakhutdinov

Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Riemannian Adaptive Optimization Methods

Gary Becigneul, Octavian-Eugen Ganea

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Andrew Brock, Jeff Donahue, Karen Simonyan

Improving MMD-GAN Training with Repulsive Loss Function

Wei Wang, Yuan Sun, Saman Halgamuge

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann

FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS

Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville

Analysing Mathematical Reasoning Abilities of Neural Models

David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli

Learning Representations of Sets through Optimized Permutations

Yan Zhang, Jonathon Hare, Adam Prugel-Bennett

Measuring Compositionality in Representation Learning

Jacob Andreas

Detecting Egregious Responses in Neural Sequence-to-sequence Models

Tianxing He, James Glass

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn

A Universal Music Translation Network

Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman

Fluctuation-dissipation relations for stochastic gradient descent

Sho Yaida

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Florian Mai, Lukas Galke, Ansgar Scherp

Adaptive Estimators Show Information Compression in Deep Neural Networks

Ivan Chelombiev, Conor Houghton, Cian O'Donnell

Universal Successor Features Approximators

Diana Borsa, Andre Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt

Deep Convolutional Networks as shallow Gaussian Processes

Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison

Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal

On the loss landscape of a class of deep neural networks with no bad local valleys

Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein

h-detach: Modifying the LSTM Gradient Towards Better Optimization

Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas

Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network

Xuanqing Liu, Yao Li, Chongruo Wu, Cho-jui Hsieh

Variational Bayesian Phylogenetic Inference

Cheng Zhang, Frederick A. Matsen IV

Relational Forward Models for Multi-Agent Learning

Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, János Kramár

Generative predecessor models for sample-efficient imitation learning

Yannick Schroecker, Mel Vecerik, Jon Scholz

Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution

Min Liu, Fupin Yao, Chiho Choi, Ayan Sinha, Karthik Ramani

Hyperbolic Attention Networks

Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu

Structured Neural Summarization

Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt

Multilingual Neural Machine Translation with Knowledge Distillation

Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar

Emergent Coordination Through Competition

Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess

Meta-Learning Update Rules for Unsupervised Representation Learning

Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-dickstein

Knowledge Flow: Improve Upon Your Teachers

Iou-Jen Liu, Jian Peng, Alexander Schwing

How Important is a Neuron

Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan

Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning

Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel

Soft Q-Learning with Mutual-Information Regularization

Jordi Grau-moya, Felix Leibfried, Peter Vrancx

Representation Degeneration Problem in Training Natural Language Generation Models

Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang

NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning

Sirui Xie, Junning Huang, Lanxin Lei, Chunxiao Liu, Zheng Ma

Latent Convolutional Models

ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky

SNAS: stochastic neural architecture search

Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin

Understanding Composition of Word Embeddings via Tensor Decomposition

Abraham Frandsen, Rong Ge

Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces

Senthil Purushwalkam, Abhinav Gupta, Danny Kaufman, Bryan Russell

Stable Opponent Shaping in Differentiable Games

Alistair Letcher, Jakob Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson

Learning Robust Representations by Projecting Superficial Statistics Out

Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing

MARGINALIZED AVERAGE ATTENTIONAL NETWORK FOR WEAKLY-SUPERVISED LEARNING

Yuan Yuan, Yueming LYU, Xi SHEN, Ivor W. Tsang, Dit-Yan Yeung

signSGD with Majority Vote is Communication Efficient and Fault Tolerant

Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar

DeepOBS: A Deep Learning Optimizer Benchmark Suite

Frank Schneider, Lukas Balles, Philipp Hennig

Policy Transfer with Strategy Optimization

Wenhao Yu, C. Karen Liu, Greg Turk

Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

CHIH-YAO MA, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira

RNNs implicitly implement tensor-product representations

R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky

Neural network gradient-based learning of black-box function interfaces

Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, Huan Zhang

Integer Networks for Data Compression with Latent-Variable Models

Johannes Ballé, Nick Johnston, David Minnen

Kernel RNN Learning (KeRNL)

Christopher Roth, Ingmar Kanitscheider, Ila Fiete

Residual Non-local Attention Networks for Image Restoration

Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, Yun Fu

Wizard of Wikipedia: Knowledge-Powered Conversational Agents

Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli

Invariant and Equivariant Graph Networks

Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman

Generalized Tensor Models for Recurrent Neural Networks

Valentin Khrulkov, Oleksii Hrinchuk, Ivan Oseledets