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Anirudh Goyal

Learning Neural Causal Models with Active Interventions


Sep 06, 2021
Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke

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Discrete-Valued Neural Communication


Jul 10, 2021
Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio

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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning


Jul 02, 2021
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Michael Mozer, Christopher Pal

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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures


Jun 14, 2021
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

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* 10 pages, 6 figures 
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Robust Representation Learning via Perceptual Similarity Metrics


Jun 11, 2021
Saeid Asgari Taghanaki, Kristy Choi, Amir Khasahmadi, Anirudh Goyal

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* Accepted to ICML 2021 
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Fast and Slow Learning of Recurrent Independent Mechanisms


May 19, 2021
Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

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* Accepted at ICLR'21 
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Neural Production Systems


Mar 02, 2021
Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio

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Coordination Among Neural Modules Through a Shared Global Workspace


Mar 01, 2021
Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio

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Transformers with Competitive Ensembles of Independent Mechanisms


Feb 27, 2021
Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, Yoshua Bengio

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* Under Review, ICML 2021 
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Towards Causal Representation Learning


Feb 22, 2021
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio

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* Special Issue of Proceedings of the IEEE - Advances in Machine Learning and Deep Neural Networks 
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Inductive Biases for Deep Learning of Higher-Level Cognition


Dec 07, 2020
Anirudh Goyal, Yoshua Bengio

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* This document contains a review of authors research as part of the requirement of AG's predoctoral exam, an overview of the main contributions of the authors few recent papers (co-authored with several other co-authors) as well as a vision of proposed future research 
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Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers


Oct 15, 2020
Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio

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


Oct 08, 2020
Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wüthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer

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* The first two authors contributed equally 
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S2RMs: Spatially Structured Recurrent Modules


Jul 13, 2020
Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

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Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems


Jun 30, 2020
Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer

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* Under Review, NeurIPS 2020 
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Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules


Jun 30, 2020
Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio

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* ICML 2020 
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Maximum Entropy Models for Fast Adaptation


Jun 30, 2020
Samarth Sinha, Anirudh Goyal, Animesh Garg

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Untangling tradeoffs between recurrence and self-attention in neural networks


Jun 16, 2020
Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

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Is Independence all you need? On the Generalization of Representations Learned from Correlated Data


Jun 14, 2020
Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer

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* 33 pages, 33 figures 
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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget


Apr 24, 2020
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine

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* Published as a conference paper at ICLR 2020 
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DIBS: Diversity inducing Information Bottleneck in Model Ensembles


Mar 10, 2020
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti

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* Samarth Sinha* and Homanga Bharadhwaj* contributed equally to this work. Code will be released at https://github.com/rvl-lab-utoronto/dibs 
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Top-K Training of GANs: Improving Generators by Making Critics Less Critical


Feb 14, 2020
Samarth Sinha, Anirudh Goyal, Colin Raffel, Augustus Odena

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Small-GAN: Speeding Up GAN Training Using Core-sets


Oct 29, 2019
Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena

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Learning Neural Causal Models from Unknown Interventions


Oct 02, 2019
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio

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Recurrent Independent Mechanisms


Sep 26, 2019
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf

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Learning the Arrow of Time


Jul 02, 2019
Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio

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* A shorter version of this work was presented at the Theoretical Phyiscs for Deep Learning Workshop, ICML 2019 
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