ICLR 2019

502 papers

Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs

Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro

Variational Autoencoder with Arbitrary Conditioning

Oleg Ivanov, Michael Figurnov, Dmitry Vetrov

ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

Wei Ping, Kainan Peng, Jitong Chen

From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama

Human-level Protein Localization with Convolutional Neural Networks

Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer

Neural Program Repair by Jointly Learning to Localize and Repair

Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR

Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schuetze

Learning Protein Structure with a Differentiable Simulator

John Ingraham, Adam Riesselman, Chris Sander, Debora Marks

Local SGD Converges Fast and Communicates Little

Sebastian U. Stich

Amortized Bayesian Meta-Learning

Sachin Ravi, Alex Beatson

Sliced Wasserstein Auto-Encoders

Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde

AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods

Zhiming Zhou*, Qingru Zhang*, Guansong Lu, Hongwei Wang, Weinan Zhang

Visual Semantic Navigation using Scene Priors

Wei Yang, Xiaolong Wang, Ali Farhadi, Abhinav Gupta, Roozbeh Mottaghi

Meta-Learning with Latent Embedding Optimization

Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu

Multilingual Neural Machine Translation With Soft Decoupled Encoding

Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig

Learning To Simulate

Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker

Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies

Kenneth Marino, Abhinav Gupta, Rob Fergus, arthur szlam

Gradient descent aligns the layers of deep linear networks

Ziwei Ji, Matus Telgarsky

On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong

The role of over-parametrization in generalization of neural networks

Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu

Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors

Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu

MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders

Xuezhe Ma, Chunting Zhou, Eduard Hovy

CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild

Yang Zhang, Hassan Foroosh, Philip David, Boqing Gong

Deep learning generalizes because the parameter-function map is biased towards simple functions

Guillermo Valle-Pérez, Chico Q. Camargo, Ard A. Louis

Temporal Difference Variational Auto-Encoder

Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber

What do you learn from context? Probing for sentence structure in contextualized word representations

Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak

Learning protein sequence embeddings using information from structure

Tristan Bepler, Bonnie Berger

Learning what and where to attend

Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine

How to train your MAML

Antreas Antoniou, Harrison Edwards, Amos Storkey

Transfer Learning for Sequences via Learning to Collocate

Wanyun Cui, Guangyu Zheng, Zhiqiang Shen, Sihang Jiang, Wei Wang

Learning Implicitly Recurrent CNNs Through Parameter Sharing

Pedro Savarese, Michael Maire

Learning Actionable Representations with Goal Conditioned Policies

Dibya Ghosh, Abhishek Gupta, Sergey Levine

A Statistical Approach to Assessing Neural Network Robustness

Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

Random mesh projectors for inverse problems

Konik Kothari*, Sidharth Gupta*, Maarten V. de Hoop, Ivan Dokmanić

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu Sun

Adversarial Attacks on Graph Neural Networks via Meta Learning

Daniel Zügner, Stephan Günnemann

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

Pay Less Attention with Lightweight and Dynamic Convolutions

Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael Auli

Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency

Liqian Ma, Xu Jia, Stamatios Georgoulis, Tinne Tuytelaars, Luc Van Gool

Deep Anomaly Detection with Outlier Exposure

Dan Hendrycks, Mantas Mazeika, Thomas Dietterich

Stable Recurrent Models

John Miller, Moritz Hardt

Learning Mixed-Curvature Representations in Product Spaces

Albert Gu, Frederic Sala, Beliz Gunel, Christopher Re

Learning Programmatically Structured Representations with Perceptor Gradients

Svetlin Penkov, Subramanian Ramamoorthy

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Jonathan Frankle, Michael Carbin

Learning Exploration Policies for Navigation

Tao Chen, Saurabh Gupta, Abhinav Gupta

Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Wieland Brendel, Matthias Bethge

Three Mechanisms of Weight Decay Regularization

Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse

Theoretical Analysis of Auto Rate-Tuning by Batch Normalization

Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu

ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees

Hao He, Hao Wang, Guang-He Lee, Yonglong Tian

Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision

José Lezama

Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images

Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix

Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks

Reinhard Heckel, Paul Hand

Unsupervised Adversarial Image Reconstruction

Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari

Time-Agnostic Prediction: Predicting Predictable Video Frames

Dinesh Jayaraman, Frederik Ebert, Alexei Efros, Sergey Levine

Transferring Knowledge across Learning Processes

Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou

Environment Probing Interaction Policies

Wenxuan Zhou, Lerrel Pinto, Abhinav Gupta

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann

Generating Multi-Agent Trajectories using Programmatic Weak Supervision

Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey

Biologically-Plausible Learning Algorithms Can Scale to Large Datasets

Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models

Marc T Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S Zemel

Defensive Quantization: When Efficiency Meets Robustness

Ji Lin, Chuang Gan, Song Han

Neural Logic Machines

Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li

Unsupervised Learning of the Set of Local Maxima

Lior Wolf, Sagie Benaim, Tomer Galanti

Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization

Navid Azizan, Babak Hassibi

DHER: Hindsight Experience Replay for Dynamic Goals

Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu

Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors

Andrew Ilyas, Logan Engstrom, Aleksander Madry

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum

Meta-learning with differentiable closed-form solvers

Luca Bertinetto, João F. Henriques, Philip Torr, Andrea Vedaldi

Learning Neural PDE Solvers with Convergence Guarantees

Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

Harmonic Unpaired Image-to-image Translation

Rui Zhang, Tomas Pfister, Jia Li

BA-Net: Dense Bundle Adjustment Networks

Chengzhou Tang, Ping Tan

Supervised Community Detection with Line Graph Neural Networks

Zhengdao Chen, Lisha Li, Joan Bruna

Robustness May Be at Odds with Accuracy

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry

Variance Reduction for Reinforcement Learning in Input-Driven Environments

Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach

Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

Towards Metamerism via Foveated Style Transfer

Arturo Deza, Aditya Jonnalagadda, Miguel P. Eckstein

Kernel Change-point Detection with Auxiliary Deep Generative Models

Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos

Mode Normalization

Lucas Deecke, Iain Murray, Hakan Bilen

Predicting the Generalization Gap in Deep Networks with Margin Distributions

YiDing Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio

Explaining Image Classifiers by Counterfactual Generation

Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse

Unsupervised Control Through Non-Parametric Discriminative Rewards

David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen

Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control

Róbert Csordás, Juergen Schmidhuber

Subgradient Descent Learns Orthogonal Dictionaries

Yu Bai, Qijia Jiang, Ju Sun

Improving the Generalization of Adversarial Training with Domain Adaptation

Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft

A Direct Approach to Robust Deep Learning Using Adversarial Networks

Huaxia Wang, Chun-Nam Yu

Preferences Implicit in the State of the World

Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan

Lagging Inference Networks and Posterior Collapse in Variational Autoencoders

Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick

Learning what you can do before doing anything

Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel

Spreading vectors for similarity search

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Herve Jegou

Probabilistic Planning with Sequential Monte Carlo methods

Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal

FlowQA: Grasping Flow in History for Conversational Machine Comprehension

Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih

Information asymmetry in KL-regularized RL

Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz

Diversity-Sensitive Conditional Generative Adversarial Networks

Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee

Fixup Initialization: Residual Learning Without Normalization

Hongyi Zhang, Yann N. Dauphin, Tengyu Ma

Recall Traces: Backtracking Models for Efficient Reinforcement Learning

Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap