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An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol, Reza Babanezhad Harikandeh, Yoshua Bengio +1
Consider a collection of datasets generated by unknown interventions on an unknown structural causal model . Recently, Bengio et al. (2020) conjectured that among all candidate…
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…
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
Devansh Arpit, Huan Wang, Caiming Xiong +2
We introduce a parameterization method called Neural Bayes which allows computing statistical quantities that are in general difficult to compute and opens avenues for formulating…
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…
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Alex Lamb, Vikas Verma, Kenji Kawaguchi +4
Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as a…