ICLR 2021

859 papers

Stabilized Medical Image Attacks

Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng

Mind the Pad -- CNNs Can Develop Blind Spots

Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Jun Yuan, Orion Reblitz-Richardson

Graph Convolution with Low-rank Learnable Local Filters

Xiuyuan Cheng, Zichen Miao, Qiang Qiu

Uncertainty Sets for Image Classifiers using Conformal Prediction

Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, Jitendra Malik

Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy

Akinori F Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka

Generalization in data-driven models of primary visual cortex

Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Willeke, Akshay Jagadish, Eric Wang

Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella Yu

Improving Adversarial Robustness via Channel-wise Activation Suppressing

Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma

Correcting experience replay for multi-agent communication

Sanjeevan Ahilan, Peter Dayan

UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers

Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang

Information Laundering for Model Privacy

Xinran Wang, Yu Xiang, Jun Gao, Jie Ding

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

Kashif Rasul, Abdul-saboor Sheikh, Ingmar Schuster, Urs M Bergmann, Roland Vollgraf

Mutual Information State Intrinsic Control

Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu

Locally Free Weight Sharing for Network Width Search

Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian

Long-tail learning via logit adjustment

Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit

Quantifying Differences in Reward Functions

Adam Gleave, Michael D Dennis, Shane Legg, Stuart Russell, Jan Leike

Contrastive Divergence Learning is a Time Reversal Adversarial Game

Omer Yair, Tomer Michaeli

LambdaNetworks: Modeling long-range Interactions without Attention

Irwan Bello

Sparse Quantized Spectral Clustering

Zhenyu Liao, Romain Couillet, Michael W. Mahoney

Graph-Based Continual Learning

Binh Tang, David S. Matteson

VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models

Zhisheng Xiao, Karsten Kreis, Jan Kautz, Arash Vahdat

Noise against noise: stochastic label noise helps combat inherent label noise

Pengfei Chen, Guangyong Chen, Junjie Ye, Jingwei Zhao, Pheng-Ann Heng

GAN "Steerability" without optimization

Nurit Spingarn, Ron Banner, Tomer Michaeli

Grounded Language Learning Fast and Slow

Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic

Support-set bottlenecks for video-text representation learning

Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander G Hauptmann

Undistillable: Making A Nasty Teacher That CANNOT teach students

Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie

A Good Image Generator Is What You Need for High-Resolution Video Synthesis

Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng

Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors

Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek Kamilov

Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Rewon Child

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?

Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference

Sanghyun Hong, Yigitcan Kaya, Ionut-Vlad Modoranu, Tudor Dumitras

Neural Topic Model via Optimal Transport

He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine

Tent: Fully Test-Time Adaptation by Entropy Minimization

Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell

Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters

Aston Zhang, Yi Tay, SHUAI ZHANG, Alvin Chan, Anh Tuan Luu

Geometry-Aware Gradient Algorithms for Neural Architecture Search

Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar

DDPNOpt: Differential Dynamic Programming Neural Optimizer

Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang

Neural Approximate Sufficient Statistics for Implicit Models

Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

Autoregressive Entity Retrieval

Nicola De Cao, Gautier Izacard, Sebastian Riedel, Fabio Petroni

How Benign is Benign Overfitting ?

Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip Torr

Unlearnable Examples: Making Personal Data Unexploitable

Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang

Learning-based Support Estimation in Sublinear Time

Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal

Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach

Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet

Fidelity-based Deep Adiabatic Scheduling

Eli Ovits, Lior Wolf

The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings

Elliot Meyerson, Risto Miikkulainen

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration

Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton

On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers

Kenji Kawaguchi

DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs

Aayam Kumar Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern

Mathematical Reasoning via Self-supervised Skip-tree Training

Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

Model-Based Visual Planning with Self-Supervised Functional Distances

Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples

Nils Lukas, Yuxuan Zhang, Florian Kerschbaum

Dynamic Tensor Rematerialization

Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Denis Yarats, Ilya Kostrikov, Rob Fergus

Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time

Tolga Ergen, Mert Pilanci

Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning

Dong Bok Lee, Dongchan Min, Seanie Lee, Sung Ju Hwang

Data-Efficient Reinforcement Learning with Self-Predictive Representations

Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville

Differentially Private Learning Needs Better Features (or Much More Data)

Florian Tramer, Dan Boneh

Self-Supervised Policy Adaptation during Deployment

Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel

Iterative Empirical Game Solving via Single Policy Best Response

Max Smith, Thomas Anthony, Michael Wellman

Towards Robustness Against Natural Language Word Substitutions

Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, Hong Liu

A Gradient Flow Framework For Analyzing Network Pruning

Ekdeep Singh Lubana, Robert P. Dick

Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize

Nils Wandel, Michael Weinmann, Reinhard Klein

PMI-Masking: Principled masking of correlated spans

Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-brown

Sharpness-aware Minimization for Efficiently Improving Generalization

Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

Random Feature Attention

Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah Smith

Fast Geometric Projections for Local Robustness Certification

Aymeric Fromherz, Klas Leino, Matt Fredrikson, Bryan Parno, Corina Pasareanu

Unsupervised Object Keypoint Learning using Local Spatial Predictability

Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber

Learning from Protein Structure with Geometric Vector Perceptrons

Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, Ron Dror

Dataset Inference: Ownership Resolution in Machine Learning

Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

Recurrent Independent Mechanisms

Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine

Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control

Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell

Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking

Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov

Topology-Aware Segmentation Using Discrete Morse Theory

Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, Chao Chen

Minimum Width for Universal Approximation

Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin

Structured Prediction as Translation between Augmented Natural Languages

Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille

Individually Fair Gradient Boosting

Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun

Emergent Symbols through Binding in External Memory

Taylor Whittington Webb, Ishan Sinha, Jonathan Cohen

How Does Mixup Help With Robustness and Generalization?

Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou

Behavioral Cloning from Noisy Demonstrations

Fumihiro Sasaki, Ryota Yamashina

What are the Statistical Limits of Offline RL with Linear Function Approximation?

Ruosong Wang, Dean Foster, Sham M. Kakade

Regularized Inverse Reinforcement Learning

Wonseok Jeon, Chen-Yang Su, Paul Barde, Thang Doan, Derek Nowrouzezahrai

HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark

Chaojian Li, Zhongzhi Yu, Yonggan Fu, Yongan Zhang, Yang Zhao

Memory Optimization for Deep Networks

Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Kraehenbuehl

Systematic generalisation with group invariant predictions

Faruk Ahmed, Yoshua Bengio, Harm Van Seijen, Aaron Courville

Distributional Sliced-Wasserstein and Applications to Generative Modeling

Khai Nguyen, nhật Hồ, Tung Pham, Hung Bui

PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics

Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao

Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration

Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao

CPT: Efficient Deep Neural Network Training via Cyclic Precision

Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding

Learning a Latent Simplex in Input Sparsity Time

Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David Woodruff, Samson Zhou

Predicting Infectiousness for Proactive Contact Tracing

Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss

Orthogonalizing Convolutional Layers with the Cayley Transform

Asher Trockman, J Zico Kolter

Influence Estimation for Generative Adversarial Networks

Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru

Disentangled Recurrent Wasserstein Autoencoder

JUN HAN, Martin Renqiang Min, Ligong Han, Li Erran Li, Xuan Zhang

Learning with Feature-Dependent Label Noise: A Progressive Approach

Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen

The Intrinsic Dimension of Images and Its Impact on Learning

Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein

RMSprop converges with proper hyper-parameter

Naichen Shi, Dawei Li, Mingyi Hong, Ruoyu Sun

Understanding the role of importance weighting for deep learning

Da Xu, Yuting Ye, Chuanwei Ruan

Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies

Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz