ICLR 2021

859 papers

On Self-Supervised Image Representations for GAN Evaluation

Stanislav Morozov, Andrey Voynov, Artem Babenko

MARS: Markov Molecular Sampling for Multi-objective Drug Discovery

Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang

Expressive Power of Invariant and Equivariant Graph Neural Networks

Waïss Azizian, marc lelarge

Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic

Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, Kee-eung Kim

On Statistical Bias In Active Learning: How and When to Fix It

Sebastian Farquhar, Yarin Gal, Tom Rainforth

Learning Mesh-Based Simulation with Graph Networks

Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, Peter Battaglia

Generalization bounds via distillation

Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang

Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs

Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling

Implicit Normalizing Flows

Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu

Discovering a set of policies for the worst case reward

Tom Zahavy, Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan O'Donoghue

Self-supervised Visual Reinforcement Learning with Object-centric Representations

Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius

Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods

Taiji Suzuki, Shunta Akiyama

Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, Yang Liu

Practical Real Time Recurrent Learning with a Sparse Approximation

Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan

Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs

Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel

Free Lunch for Few-shot Learning: Distribution Calibration

Shuo Yang, LU LIU, Min Xu

Geometry-aware Instance-reweighted Adversarial Training

Jingfeng ZHANG, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes

Mike Gartrell, Insu Han, Elvis DOHMATOB, Jennifer Gillenwater, Victor-Emmanuel Brunel

Dataset Condensation with Gradient Matching

Bo Zhao, Konda Reddy Mopuri, Hakan Bilen

Neural Synthesis of Binaural Speech From Mono Audio

Alexander Richard, Dejan Markovic, Israel D. Gebru, Steven Krenn, Gladstone Alexander Butler

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study

Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin

Rethinking Attention with Performers

Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-Ichi Kawarabayashi

Human-Level Performance in No-Press Diplomacy via Equilibrium Search

Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Yuan Yin, Vincent LE GUEN, Jérémie Donà, Emmanuel de Bézenac, Ibrahim Ayed

Gradient Projection Memory for Continual Learning

Gobinda Saha, Isha Garg, Kaushik Roy

Learning Generalizable Visual Representations via Interactive Gameplay

Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han

Deformable DETR: Deformable Transformers for End-to-End Object Detection

Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang

Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency

Qiang Zhang, Tete Xiao, Alexei A Efros, Lerrel Pinto, Xiaolong Wang

DiffWave: A Versatile Diffusion Model for Audio Synthesis

Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, Bryan Catanzaro

Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies

T. Konstantin Rusch, Siddhartha Mishra

Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

Max B Paulus, Chris J. Maddison, Andreas Krause

Federated Learning Based on Dynamic Regularization

Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough

Self-training For Few-shot Transfer Across Extreme Task Differences

Cheng Perng Phoo, Bharath Hariharan

Learning to Reach Goals via Iterated Supervised Learning

Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin

SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments

Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn

Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability

Suraj Srinivas, Francois Fleuret

Learning Invariant Representations for Reinforcement Learning without Reconstruction

Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine

Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime

Atsushi Nitanda, Taiji Suzuki

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai

A Distributional Approach to Controlled Text Generation

Muhammad Khalifa, Hady Elsahar, Marc Dymetman

Randomized Automatic Differentiation

Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P Adams

EigenGame: PCA as a Nash Equilibrium

Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae

Growing Efficient Deep Networks by Structured Continuous Sparsification

Xin Yuan, Pedro Henrique Pamplona Savarese, Michael Maire

Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma

SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness

Mikhail Yurochkin, Yuekai Sun

Parrot: Data-Driven Behavioral Priors for Reinforcement Learning

Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart

MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training

Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li

Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering

Yuxuan Zhang, Wenzheng Chen, huan ling, Jun Gao, Yinan Zhang

Evolving Reinforcement Learning Algorithms

John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le

Rethinking Architecture Selection in Differentiable NAS

Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-jui Hsieh

Global Convergence of Three-layer Neural Networks in the Mean Field Regime

Huy Tuan Pham, Phan-Minh Nguyen

Score-Based Generative Modeling through Stochastic Differential Equations

Yang Song, Jascha Sohl-dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon

Improved Autoregressive Modeling with Distribution Smoothing

Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Brenden K Petersen, Mikel Landajuela Larma, Terrell N. Mundhenk, Claudio Prata Santiago, Soo Kyung Kim

When Do Curricula Work?

Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur

Iterated learning for emergent systematicity in VQA

Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?

Zhiyuan Li, Yi Zhang, Sanjeev Arora

Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions

Zhengxian Lin, Kin-Ho Lam, Alan Fern

Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

JangHyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song

End-to-end Adversarial Text-to-Speech

Jeff Donahue, Sander Dieleman, Mikolaj Binkowski, Erich Elsen, Karen Simonyan

Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation

Biao Zhang, Ankur Bapna, Rico Sennrich, Orhan Firat

Complex Query Answering with Neural Link Predictors

Erik Arakelyan, Daniel Daza, Pasquale Minervini, Michael Cochez

On the mapping between Hopfield networks and Restricted Boltzmann Machines

Matthew Smart, Anton Zilman

BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu

Pruning Neural Networks at Initialization: Why Are We Missing the Mark?

Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains

Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky

BiPointNet: Binary Neural Network for Point Clouds

Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao

DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei

EEC: Learning to Encode and Regenerate Images for Continual Learning

Ali Ayub, Alan Wagner

Network Pruning That Matters: A Case Study on Retraining Variants

Duong Hoang Le, Binh-Son Hua

PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences

Hehe Fan, Xin Yu, Yuhang Ding, Yi Yang, Mohan Kankanhalli

Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples

Ziang Yan, Yiwen Guo, Jian Liang, Changshui Zhang

Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling

Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li

BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Huanrui Yang, Lin Duan, Yiran Chen, Hai Li

Scaling the Convex Barrier with Active Sets

Alessandro De Palma, Harkirat Behl, Rudy R Bunel, Philip Torr, M. Pawan Kumar

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault

Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective

Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang

PDE-Driven Spatiotemporal Disentanglement

Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari

SEED: Self-supervised Distillation For Visual Representation

Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, Yezhou Yang

Learning to Generate 3D Shapes with Generative Cellular Automata

Dongsu Zhang, Changwoon Choi, Jeonghwan Kim, Young Min Kim

Bag of Tricks for Adversarial Training

Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu

MALI: A memory efficient and reverse accurate integrator for Neural ODEs

Juntang Zhuang, Nicha C Dvornek, sekhar tatikonda, James s Duncan

DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation

Minjia Zhang, Menghao Li, Chi Wang, Mingqin Li

Revisiting Dynamic Convolution via Matrix Decomposition

Yunsheng Li, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen

Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors

Linfeng Zhang, Kaisheng Ma

MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond

Duy Kien Nguyen, Vedanuj Goswami, Xinlei Chen

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

Shape-Texture Debiased Neural Network Training

Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang

Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning

Tsung-Wei Ke, jyh-Jing Hwang, Stella Yu

DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation

Alexandre Rame, Matthieu Cord

Efficient Inference of Flexible Interaction in Spiking-neuron Networks

Feng Zhou, YIXUAN ZHANG, Jun Zhu

Domain Generalization with MixStyle

Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

Protecting DNNs from Theft using an Ensemble of Diverse Models

Sanjay Kariyappa, Atul Prakash, Moinuddin K Qureshi

Batch Reinforcement Learning Through Continuation Method

Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee

Explainable Deep One-Class Classification

Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft