122 citations · 436 across the 21 of their papers we have counts for
9 papers · 1 filter
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick +1
In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. Th…
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Samarth Sinha, Zhengli Zhao, Anirudh Goyal +2
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computatio…
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…
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…
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal +4
Deep networks have achieved impressive results across a variety of important tasks. However a known weakness is a failure to perform well when evaluated on data which differ from t…