ICLR 2021

859 papers

TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks

Martin Trimmel, Henning Petzka, Cristian Sminchisescu

Variational Information Bottleneck for Effective Low-Resource Fine-Tuning

Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson

Theoretical bounds on estimation error for meta-learning

James Lucas, Mengye Ren, Irene Raissa KAMENI KAMENI, Toniann Pitassi, Richard Zemel

Colorization Transformer

Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner

Disambiguating Symbolic Expressions in Informal Documents

Dennis Müller, Cezary Kaliszyk

Learning to Set Waypoints for Audio-Visual Navigation

Changan Chen, Sagnik Majumder, Ziad Al-Halah, Ruohan Gao, Santhosh Kumar Ramakrishnan

New Bounds For Distributed Mean Estimation and Variance Reduction

Peter Davies, Vijaykrishna Gurunanthan, Niusha Moshrefi, Saleh Ashkboos, Dan Alistarh

Parameter-Based Value Functions

Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber

Learning to Sample with Local and Global Contexts in Experience Replay Buffer

Youngmin Oh, Kimin Lee, Jinwoo Shin, Eunho Yang, Sung Ju Hwang

Multiscale Score Matching for Out-of-Distribution Detection

Ahsan Mahmood, Junier Oliva, Martin Andreas Styner

Learning Associative Inference Using Fast Weight Memory

Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber

Temporally-Extended ε-Greedy Exploration

Will Dabney, Georg Ostrovski, Andre Barreto

Differentiable Trust Region Layers for Deep Reinforcement Learning

Fabian Otto, Philipp Becker, Vien Anh Ngo, Hanna Carolin Maria Ziesche, Gerhard Neumann

Hopfield Networks is All You Need

Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich

Grounding Language to Autonomously-Acquired Skills via Goal Generation

Ahmed Akakzia, Cédric Colas, Pierre-yves Oudeyer, Mohamed CHETOUANI, Olivier Sigaud

Training independent subnetworks for robust prediction

Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek

Adversarially Guided Actor-Critic

Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks

Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann

NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition

Abhinav Mehrotra, Alberto Gil C. P. Ramos, Sourav Bhattacharya, Łukasz Dudziak, Ravichander Vipperla

Learning continuous-time PDEs from sparse data with graph neural networks

Valerii Iakovlev, Markus Heinonen, Harri Lahdesmaki

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech

Yoonhyung Lee, Joongbo Shin, Kyomin Jung

CoCon: A Self-Supervised Approach for Controlled Text Generation

Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, Jie Fu

Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks

Christian H.X. Ali Mehmeti-Göpel, David Hartmann, Michael Wand

SALD: Sign Agnostic Learning with Derivatives

Matan Atzmon, Yaron Lipman

Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units

Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquette, Dimitri Michael Kullmann

The role of Disentanglement in Generalisation

Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, Jeffrey Bowers

Neural ODE Processes

Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lió

Transformer protein language models are unsupervised structure learners

Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, Alexander Rives

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich

Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models

Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt

Generalized Variational Continual Learning

Noel Loo, Siddharth Swaroop, Richard E Turner

ResNet After All: Neural ODEs and Their Numerical Solution

Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann

Uncertainty-aware Active Learning for Optimal Bayesian Classifier

Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian

Neural networks with late-phase weights

Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning

Mapping the Timescale Organization of Neural Language Models

Hsiang-Yun Sherry Chien, Jinhan Zhang, Christopher Honey

Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization

Joshua C Chang, Patrick Fletcher, Jungmin Han, Ted L Chang, Shashaank Vattikuti

Representation Learning via Invariant Causal Mechanisms

Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, Charles Blundell

A Block Minifloat Representation for Training Deep Neural Networks

Sean Fox, Seyedramin Rasoulinezhad, Julian Faraone, david boland, Philip Leong

Overfitting for Fun and Profit: Instance-Adaptive Data Compression

Ties van Rozendaal, Iris AM Huijben, Taco Cohen

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Jonas Geiping, Liam H Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor

Continual learning in recurrent neural networks

Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes von Oswald

On the geometry of generalization and memorization in deep neural networks

Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang

Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning

Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret

DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION

Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen

Efficient Generalized Spherical CNNs

Oliver Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price

Gradient Origin Networks

Sam Bond-Taylor, Chris G. Willcocks

Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering

Calypso Herrera, Florian Krach, Josef Teichmann

Modeling the Second Player in Distributionally Robust Optimization

Paul Michel, Tatsunori Hashimoto, Graham Neubig

Self-supervised Adversarial Robustness for the Low-label, High-data Regime

Sven Gowal, Po-Sen Huang, Aaron van den Oord, Timothy Mann, Pushmeet Kohli

Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana

Learning from others' mistakes: Avoiding dataset biases without modeling them

Victor Sanh, Thomas Wolf, Yonatan Belinkov, Alexander M Rush

Robust Reinforcement Learning on State Observations with Learned Optimal Adversary

Huan Zhang, Hongge Chen, Duane S Boning, Cho-jui Hsieh

On the Transfer of Disentangled Representations in Realistic Settings

Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal

Certify or Predict: Boosting Certified Robustness with Compositional Architectures

Mark Niklas Mueller, Mislav Balunovic, Martin Vechev

A teacher-student framework to distill future trajectories

Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing

Tao Yu, Rui Zhang, Alex Polozov, Christopher Meek, Ahmed Hassan Awadallah

Optimism in Reinforcement Learning with Generalized Linear Function Approximation

Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy

Predicting Inductive Biases of Pre-Trained Models

Charles Lovering, Rohan Jha, Tal Linzen, Ellie Pavlick

Net-DNF: Effective Deep Modeling of Tabular Data

Liran Katzir, Gal Elidan, Ran El-Yaniv