ICLR 2021

859 papers

Learnable Embedding sizes for Recommender Systems

Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, Yong Li

Symmetry-Aware Actor-Critic for 3D Molecular Design

Gregor N. C. Simm, Robert Pinsler, Gabor Csanyi, José Miguel Hernández-Lobato

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh

More or Less: When and How to Build Convolutional Neural Network Ensembles

Abdul Wasay, Stratos Idreos

Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning

Da Yu, Huishuai Zhang, Wei Chen, Tie-yan Liu

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis

Zhong Li, Jiequn Han, Weinan E, Qianxiao Li

Progressive Skeletonization: Trimming more fat from a network at initialization

Pau De Jorge, Amartya Sanyal, Harkirat Behl, Philip Torr, Gregory Rogez

Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation

Yaling Tao, Kentaro Takagi, Kouta Nakata

Domain-Robust Visual Imitation Learning with Mutual Information Constraints

Edoardo Cetin, Oya Celiktutan

Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors

Ali Harakeh, Steven L. Waslander

AdaSpeech: Adaptive Text to Speech for Custom Voice

Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin

ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations

Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, Deepak Gupta

Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates

Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen, Chuchu Fan

Isometric Transformation Invariant and Equivariant Graph Convolutional Networks

Masanobu Horie, Naoki Morita, Toshiaki Hishinuma, Yu Ihara, NAOTO MITSUME

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau

Intraclass clustering: an implicit learning ability that regularizes DNNs

Simon Carbonnelle, Christophe De Vleeschouwer

CT-Net: Channel Tensorization Network for Video Classification

Kunchang Li, Xianhang Li, Yali Wang, Jun Wang, Yu Qiao

Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels

Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne

Self-supervised Learning from a Multi-view Perspective

Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency

EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS

Like Hui, Mikhail Belkin

Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory

Jason Ramapuram, Yan Wu, Alexandros Kalousis

Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks

Timothy Castiglia, Anirban Das, Stacy Patterson

Efficient Wasserstein Natural Gradients for Reinforcement Learning

Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

On the Impossibility of Global Convergence in Multi-Loss Optimization

Alistair Letcher

BREEDS: Benchmarks for Subpopulation Shift

Shibani Santurkar, Dimitris Tsipras, Aleksander Madry

Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers

Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Georg Martius

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

IOT: Instance-wise Layer Reordering for Transformer Structures

Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin

Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation

Emilio Parisotto, Russ Salakhutdinov

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

Mastering Atari with Discrete World Models

Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba

Learning a Latent Search Space for Routing Problems using Variational Autoencoders

André Hottung, Bhanu Bhandari, Kevin Tierney

Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule

Shuhei Kurita, Kyunghyun Cho

Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

Pedro Hermosilla, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez

No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks

Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi

Deconstructing the Regularization of BatchNorm

Yann Dauphin, Ekin Dogus Cubuk

Personalized Federated Learning with First Order Model Optimization

Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, Jose M. Alvarez

Zero-Cost Proxies for Lightweight NAS

Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, Nicholas Donald Lane

On the role of planning in model-based deep reinforcement learning

Jessica B Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Michael R Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker

AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly

Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo

Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability

Jeremy Cohen, Simran Kaur, Yuanzhi Li, J Zico Kolter, Ameet Talwalkar

CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks

Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei

A Learning Theoretic Perspective on Local Explainability

Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar

Go with the flow: Adaptive control for Neural ODEs

Mathieu Chalvidal, Matthew Ricci, Rufin VanRullen, Thomas Serre

Using latent space regression to analyze and leverage compositionality in GANs

Lucy Chai, Jonas Wulff, Phillip Isola

InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia

Group Equivariant Generative Adversarial Networks

Neel Dey, Antong Chen, Soheil Ghafurian

Separation and Concentration in Deep Networks

John Zarka, Florentin Guth, Stephane Mallat

Optimal Regularization can Mitigate Double Descent

Preetum Nakkiran, Prayaag Venkat, Sham M. Kakade, Tengyu Ma

Neural Thompson Sampling

Weitong ZHANG, Dongruo Zhou, Lihong Li, Quanquan Gu

Communication in Multi-Agent Reinforcement Learning: Intention Sharing

Woojun Kim, Jongeui Park, Youngchul Sung

Conditional Negative Sampling for Contrastive Learning of Visual Representations

Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah Goodman

Learning and Evaluating Representations for Deep One-Class Classification

Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister

Evaluations and Methods for Explanation through Robustness Analysis

Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim

UMEC: Unified model and embedding compression for efficient recommendation systems

Jiayi Shen, Haotao Wang, Shupeng Gui, Jianchao Tan, Zhangyang Wang

Fooling a Complete Neural Network Verifier

Dániel Zombori, Balázs Bánhelyi, Tibor Csendes, István Megyeri, Márk Jelasity

Learning to Recombine and Resample Data For Compositional Generalization

Ekin Akyürek, Afra Feyza Akyürek, Jacob Andreas

Meta-learning with negative learning rates

Alberto Bernacchia

Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning

Siyang Yuan, Pengyu Cheng, Ruiyi Zhang, Weituo Hao, Zhe Gan

Overparameterisation and worst-case generalisation: friend or foe?

Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

Generative Time-series Modeling with Fourier Flows

Ahmed Alaa, Alex James Chan, Mihaela van der Schaar

Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

Yingxue Zhou, Steven Wu, Arindam Banerjee

Teaching Temporal Logics to Neural Networks

Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus Norman Rabe, Bernd Finkbeiner

Byzantine-Resilient Non-Convex Stochastic Gradient Descent

Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh

Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Shyam Anil Tailor, Javier Fernández-Marqués, Nicholas Donald Lane

Learning explanations that are hard to vary

Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf

Class Normalization for (Continual)? Generalized Zero-Shot Learning

Ivan Skorokhodov, Mohamed Elhoseiny

Aligning AI With Shared Human Values

Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li

No MCMC for me: Amortized sampling for fast and stable training of energy-based models

Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky

Measuring Massive Multitask Language Understanding

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika

Uncertainty Estimation in Autoregressive Structured Prediction

Andrey Malinin, Mark Gales

Attentional Constellation Nets for Few-Shot Learning

Weijian Xu, Yifan Xu, Huaijin Wang, Zhuowen Tu

Meta Back-Translation

Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig

Calibration tests beyond classification

David Widmann, Fredrik Lindsten, Dave Zachariah

Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks

Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber

FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning

Hong-You Chen, Wei-Lun Chao

Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online

Yangchen Pan, Kirby Banman, Martha White

Learning A Minimax Optimizer: A Pilot Study

Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu

DeLighT: Deep and Light-weight Transformer

Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi

Hierarchical Autoregressive Modeling for Neural Video Compression

Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit

Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning

Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang

Does enhanced shape bias improve neural network robustness to common corruptions?

Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer

Noise or Signal: The Role of Image Backgrounds in Object Recognition

Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry

A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks

Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora

Neural representation and generation for RNA secondary structures

Zichao Yan, William L. Hamilton, Mathieu Blanchette

SOLAR: Sparse Orthogonal Learned and Random Embeddings

Tharun Medini, Beidi Chen, Anshumali Shrivastava

Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

Karan Goel, Albert Gu, Yixuan Li, Christopher Re

Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds

Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu

A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels

Leon Lang, Maurice Weiler

Can a Fruit Fly Learn Word Embeddings?

Yuchen Liang, Chaitanya Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha

Single-Photon Image Classification

Thomas Fischbacher, Luciano Sbaiz

Robust Pruning at Initialization

Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Alex Tamkin, Mike Wu, Noah Goodman

Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation

Justin Fu, Sergey Levine

DrNAS: Dirichlet Neural Architecture Search

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

A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning

Samuel Horváth, Peter Richtarik

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers

Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime

Andrea Agazzi, Jianfeng Lu