ICLR 2018

337 papers

Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration

Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang

An image representation based convolutional network for DNA classification

Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schoenhuth, Sander Bohte

Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks

Youngjin Kim, Minjung Kim, Gunhee Kim

Regularizing and Optimizing LSTM Language Models

Stephen Merity, Nitish Shirish Keskar, Richard Socher

On the Expressive Power of Overlapping Architectures of Deep Learning

Or Sharir, Amnon Shashua

Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang

Model compression via distillation and quantization

Antonio Polino, Razvan Pascanu, Dan Alistarh

Temporal Difference Models: Model-Free Deep RL for Model-Based Control

Vitchyr Pong*, Shixiang Gu*, Murtaza Dalal, Sergey Levine

Learning Parametric Closed-Loop Policies for Markov Potential Games

Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo

Unsupervised Cipher Cracking Using Discrete GANs

Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama

i-RevNet: Deep Invertible Networks

Jorn-Henrik Jacobsen, Arnold W.M. Smeulders, Edouard Oyallon

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design

Yoav Levine, David Yakira, Nadav Cohen, Amnon Shashua

Wavelet Pooling for Convolutional Neural Networks

Travis Williams, Robert Li

Memory Architectures in Recurrent Neural Network Language Models

Dani Yogatama, Yishu Miao, Gábor Melis, Wang Ling, Adhiguna Kuncoro

Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning

Tianmin Shu, Caiming Xiong, Richard Socher

Sobolev GAN

Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng

Variational Message Passing with Structured Inference Networks

Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan

Generating Wikipedia by Summarizing Long Sequences

Peter J. Liu*, Mohammad Saleh*, Etienne Pot, Ben Goodrich, Ryan Sepassi

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Wieland Brendel *, Jonas Rauber *, Matthias Bethge

Reinforcement Learning Algorithm Selection

Romain Laroche, Raphaël Féraud

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen

Learning Robust Rewards with Adverserial Inverse Reinforcement Learning

Justin Fu, Katie Luo, Sergey Levine

Parallelizing Linear Recurrent Neural Nets Over Sequence Length

Eric Martin, Chris Cundy

Lifelong Learning with Dynamically Expandable Networks

Jaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju Hwang

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings

Tomer Galanti, Lior Wolf, Sagie Benaim

Unsupervised Representation Learning by Predicting Image Rotations

Spyros Gidaris, Praveer Singh, Nikos Komodakis

Graph Attention Networks

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lió

Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks

Jinsung Yoon, William R. Zame, Mihaela van der Schaar

Model-Ensemble Trust-Region Policy Optimization

Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Jie Chen, Tengfei Ma, Cao Xiao

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

SHIYU LIANG, Yixuan Li, R. Srikant

Distributed Prioritized Experience Replay

Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel

Skip Connections Eliminate Singularities

Emin Orhan, Xaq Pitkow

Eigenoption Discovery through the Deep Successor Representation

Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro

Training and Inference with Integers in Deep Neural Networks

Shuang Wu, Guoqi Li, Feng Chen, Luping Shi

Parametrized Hierarchical Procedures for Neural Programming

Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Aleksandar Bojchevski, Stephan Günnemann

Unsupervised Neural Machine Translation

Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, Liqiang Wang

Learning to Represent Programs with Graphs

Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi

On Unifying Deep Generative Models

Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing

Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning

Clemens Rosenbaum, Tim Klinger, Matthew Riemer

Spectral Normalization for Generative Adversarial Networks

Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida

Improving GANs Using Optimal Transport

Tim Salimans, Han Zhang, Alec Radford, Dimitris Metaxas

Understanding Deep Neural Networks with Rectified Linear Units

Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher J Pal

Fidelity-Weighted Learning

Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf

Initialization matters: Orthogonal Predictive State Recurrent Neural Networks

Krzysztof Choromanski, Carlton Downey, Byron Boots

Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation

Xu He, Herbert Jaeger

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Spatially Transformed Adversarial Examples

Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, mingyan liu

Learning to cluster in order to transfer across domains and tasks

Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira

Learning an Embedding Space for Transferable Robot Skills

Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

Mitigating Adversarial Effects Through Randomization

Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille

Not-So-Random Features

Brian Bullins, Cyril Zhang, Yi Zhang

Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip

Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu

Gradient Estimators for Implicit Models

Yingzhen Li, Richard E. Turner

Mixed Precision Training of Convolutional Neural Networks using Integer Operations

Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Yujun Lin, Song Han, Huizi Mao, Yu Wang, Bill Dally

An Online Learning Approach to Generative Adversarial Networks

Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, Andreas Krause

Proximal Backpropagation

Thomas Frerix, Thomas Möllenhoff, Michael Moeller, Daniel Cremers

Decoupling the Layers in Residual Networks

Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas Griffiths

Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch

Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity

Tianyi Zhou, Jeff Bilmes

Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction

Da Xiao, Jo-Yu Liao, Xingyuan Yuan

Generalizing Hamiltonian Monte Carlo with Neural Networks

Daniel Levy, Matt D. Hoffman, Jascha Sohl-dickstein

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui, Ruitong Huang

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

Multi-Task Learning for Document Ranking and Query Suggestion

Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang

cGANs with Projection Discriminator

Takeru Miyato, Masanori Koyama

Towards Neural Phrase-based Machine Translation

Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng

mixup: Beyond Empirical Risk Minimization

Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-paz

Efficient Sparse-Winograd Convolutional Neural Networks

Xingyu Liu, Jeff Pool, Song Han, William J. Dally

SCAN: Learning Hierarchical Compositional Visual Concepts

Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess

Detecting Statistical Interactions from Neural Network Weights

Michael Tsang, Dehua Cheng, Yan Liu

Adversarial Dropout Regularization

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko

On the Information Bottleneck Theory of Deep Learning

Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky

Interactive Grounded Language Acquisition and Generalization in a 2D World

Haonan Yu, Haichao Zhang, Wei Xu

On the regularization of Wasserstein GANs

Henning Petzka, Asja Fischer, Denis Lukovnikov

SMASH: One-Shot Model Architecture Search through HyperNetworks

Andrew Brock, Theo Lim, J.M. Ritchie, Nick Weston

Learn to Pay Attention

Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr

Neural Sketch Learning for Conditional Program Generation

Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields

Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel

HexaConv

Emiel Hoogeboom, Jorn W.T. Peters, Taco S. Cohen, Max Welling

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning

Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ö. Arik, Ajay Kannan

A New Method of Region Embedding for Text Classification

Chao Qiao, Bo Huang, Guocheng Niu, Daren Li, Daxiang Dong

LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING

Dejiao Zhang, Haozhu Wang, Mário Figueiredo, Laura Balzano

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions

Scott Reed, Yutian Chen, Thomas Paine, Aaron van den Oord, S. M. Ali Eslami

Deep Learning with Logged Bandit Feedback

Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

Hyperparameter optimization: a spectral approach

Elad Hazan, Adam Klivans, Yang Yuan

Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples

Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain

Noisy Networks For Exploration

Meire Fortunato, Mohammad Gheshlaghi azar, Bilal Piot, Jacob Menick, Matteo Hessel

Distributed Fine-tuning of Language Models on Private Data

Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky

Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks

Pratik Chaudhari, Stefano Soatto