ICLR 2019

502 papers

Learning deep representations by mutual information estimation and maximization

R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman

Information-Directed Exploration for Deep Reinforcement Learning

Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause

Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs

Sachin Kumar, Yulia Tsvetkov

Bayesian Policy Optimization for Model Uncertainty

Gilwoo Lee, Brian Hou, Aditya Mandalika, Jeongseok Lee, Sanjiban Choudhury

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh

Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods

Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

Episodic Curiosity through Reachability

Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys

From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference

Randall Balestriero, Richard Baraniuk

A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stephane Deny

Learning-Based Frequency Estimation Algorithms

Chen-Yu Hsu, Piotr Indyk, Dina Katabi, Ali Vakilian

Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience

Vaishnavh Nagarajan, Zico Kolter

Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer

Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson

Dynamic Channel Pruning: Feature Boosting and Suppression

Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-Zhong Xu

Solving the Rubik's Cube with Approximate Policy Iteration

Stephen Mcaleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi

Diversity is All You Need: Learning Skills without a Reward Function

Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

Unsupervised Hyper-alignment for Multilingual Word Embeddings

Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin

Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation

Sang-Woo Lee, Tong Gao, Sohee Yang, Jaejun Yoo, Jung-Woo Ha

Multi-class classification without multi-class labels

Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira

Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach

Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, Jinfeng Yi

Recurrent Experience Replay in Distributed Reinforcement Learning

Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney

Unsupervised Learning via Meta-Learning

Kyle Hsu, Sergey Levine, Chelsea Finn

Discriminator Rejection Sampling

Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, Augustus Odena

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi

Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality

Taiji Suzuki

Excessive Invariance Causes Adversarial Vulnerability

Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge

GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING

Jacob Menick, Nal Kalchbrenner

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

Stanisław Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio

The Laplacian in RL: Learning Representations with Efficient Approximations

Yifan Wu, George Tucker, Ofir Nachum

A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery

Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

Adversarial Audio Synthesis

Chris Donahue, Julian McAuley, Miller Puckette

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Michael Lutter, Christian Ritter, Jan Peters

Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition

Chun-Fu (Richard) Chen, Quanfu Fan, Neil Mallinar, Tom Sercu, Rogerio Feris

On Computation and Generalization of Generative Adversarial Networks under Spectrum Control

Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang

Quasi-hyperbolic momentum and Adam for deep learning

Jerry Ma, Denis Yarats

Deep Frank-Wolfe For Neural Network Optimization

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

No Training Required: Exploring Random Encoders for Sentence Classification

John Wieting, Douwe Kiela

Measuring and regularizing networks in function space

Ari Benjamin, David Rolnick, Konrad Kording

Learning Finite State Representations of Recurrent Policy Networks

Anurag Koul, Alan Fern, Sam Greydanus

Analyzing Inverse Problems with Invertible Neural Networks

Lynton Ardizzone, Jakob Kruse, Carsten Rother, Ullrich Köthe

AutoLoss: Learning Discrete Schedule for Alternate Optimization

Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov

On Self Modulation for Generative Adversarial Networks

Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly

Spectral Inference Networks: Unifying Deep and Spectral Learning

David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld

How Powerful are Graph Neural Networks?

Keyulu Xu*, Weihua Hu*, Jure Leskovec, Stefanie Jegelka

Learning Two-layer Neural Networks with Symmetric Inputs

Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

Neural Probabilistic Motor Primitives for Humanoid Control

Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham

The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure

Frederic Koehler, Andrej Risteski

Phase-Aware Speech Enhancement with Deep Complex U-Net

Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh, Adrian Kim, Jung-Woo Ha

Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation

Soochan Lee, Junsoo Ha, Gunhee Kim

Learning Multimodal Graph-to-Graph Translation for Molecule Optimization

Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

Unsupervised Discovery of Parts, Structure, and Dynamics

Zhenjia Xu*, Zhijian Liu*, Chen Sun, Kevin Murphy, William T. Freeman

Scalable Unbalanced Optimal Transport using Generative Adversarial Networks

Karren D. Yang, Caroline Uhler

ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks

Mingzhang Yin, Mingyuan Zhou

Deep Layers as Stochastic Solvers

Adel Bibi, Bernard Ghanem, Vladlen Koltun, Rene Ranftl

Graph HyperNetworks for Neural Architecture Search

Chris Zhang, Mengye Ren, Raquel Urtasun

Learning Self-Imitating Diverse Policies

Tanmay Gangwani, Qiang Liu, Jian Peng

Slimmable Neural Networks

Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang

BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning

Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia

Complement Objective Training

Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan

ProMP: Proximal Meta-Policy Search

Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel

Learning Multi-Level Hierarchies with Hindsight

Andrew Levy, George Konidaris, Robert Platt, Kate Saenko

Discovery of Natural Language Concepts in Individual Units of CNNs

Seil Na, Yo Joong Choe, Dong-Hyun Lee, Gunhee Kim

Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity

Thomas Miconi, Aditya Rawal, Jeff Clune, Kenneth O. Stanley

A2BCD: Asynchronous Acceleration with Optimal Complexity

Robert Hannah, Fei Feng, Wotao Yin

An Empirical study of Binary Neural Networks' Optimisation

Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal

Universal Transformers

Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, LUKASZ KAISER

LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING

Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang

Improving Sequence-to-Sequence Learning via Optimal Transport

Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan

Verification of Non-Linear Specifications for Neural Networks

Chongli Qin, Krishnamurthy (Dj) Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset

Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang

Variational Autoencoders with Jointly Optimized Latent Dependency Structure

Jiawei He, Yu Gong, Joseph Marino, Greg Mori, Andreas Lehrmann

A comprehensive, application-oriented study of catastrophic forgetting in DNNs

B. Pfülb, A. Gepperth

Efficient Training on Very Large Corpora via Gramian Estimation

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang

Unsupervised Domain Adaptation for Distance Metric Learning

Kihyuk Sohn, Wenling Shang, Xiang Yu, Manmohan Chandraker

Learning to Represent Edits

Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt

Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan

Decoupled Weight Decay Regularization

Ilya Loshchilov, Frank Hutter

Generative Question Answering: Learning to Answer the Whole Question

Mike Lewis, Angela Fan

Hierarchical Generative Modeling for Controllable Speech Synthesis

Wei-Ning Hsu, Yu Zhang, Ron J. Weiss, Heiga Zen, Yonghui Wu

Exploration by random network distillation

Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov

FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models

Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

Learning concise representations for regression by evolving networks of trees

William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore

Diversity and Depth in Per-Example Routing Models

Prajit Ramachandran, Quoc V. Le

Feature Intertwiner for Object Detection

Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang

Optimal Completion Distillation for Sequence Learning

Sara Sabour, William Chan, Mohammad Norouzi

ProxQuant: Quantized Neural Networks via Proximal Operators

Yu Bai, Yu-Xiang Wang, Edo Liberty

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Amanpreet Singh, Tushar Jain, Sainbayar Sukhbaatar

Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching

Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu

Generalizable Adversarial Training via Spectral Normalization

Farzan Farnia, Jesse Zhang, David Tse

Wasserstein Barycenter Model Ensembling

Pierre Dognin*, Igor Melnyk*, Youssef Mroueh*, Jarret Ross*, Cicero Dos Santos*

Learning Embeddings into Entropic Wasserstein Spaces

Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon

Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer

Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf

InfoBot: Transfer and Exploration via the Information Bottleneck

Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle

Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds

Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus

Learning to Describe Scenes with Programs

Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum

InstaGAN: Instance-aware Image-to-Image Translation

Sangwoo Mo, Minsu Cho, Jinwoo Shin

DOM-Q-NET: Grounded RL on Structured Language

Sheng Jia, Jamie Ryan Kiros, Jimmy Ba

A new dog learns old tricks: RL finds classic optimization algorithms

Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar

The Deep Weight Prior

Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitriy Vetrov, Max Welling