328 citations · 1.6k across the 61 of their papers we have counts for
11 papers · 1 filter
Small-GAN: Speeding Up GAN Training Using Core-sets
Samarth Sinha, Han Zhang, Anirudh Goyal +3
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches i…
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal +6
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theo…
Recurrent Independent Mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann +4
Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying caus…
Learning the Arrow of Time
Nasim Rahaman, Steffen Wolf, Anirudh Goyal +2
We humans seem to have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment. Drawing inspir…
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Anirudh Goyal, Shagun Sodhani, Jonathan Binas +3
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the cont…
Learning Powerful Policies by Using Consistent Dynamics Model
Shagun Sodhani, Anirudh Goyal, Tristan Deleu +3
Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has been made by learning models vi…
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal +5
Machine learning promises methods that generalize well from finite labeled data. However, the brittleness of existing neural net approaches is revealed by notable failures, such as…
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati +4
In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to pro…
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman +5
We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of…
InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse +5
A central challenge in reinforcement learning is discovering effective policies for tasks where rewards are sparsely distributed. We postulate that in the absence of useful reward…
Maximum Entropy Generators for Energy-Based Models
Rithesh Kumar, Sherjil Ozair, Anirudh Goyal +2
Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In this work, we propose learning both the e…