66 research outputs found
CEB Improves Model Robustness
We demonstrate that the Conditional Entropy Bottleneck (CEB) can improve
model robustness. CEB is an easy strategy to implement and works in tandem with
data augmentation procedures. We report results of a large scale adversarial
robustness study on CIFAR-10, as well as the ImageNet-C Common Corruptions
Benchmark, ImageNet-A, and PGD attacks
Information in Infinite Ensembles of Infinitely-Wide Neural Networks
In this preliminary work, we study the generalization properties of infinite
ensembles of infinitely-wide neural networks. Amazingly, this model family
admits tractable calculations for many information-theoretic quantities. We
report analytical and empirical investigations in the search for signals that
correlate with generalization.Comment: 2nd Symposium on Advances in Approximate Bayesian Inference, 201
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