A Bayesian Perspective on Generalization and Stochastic Gradient Descent

17 Oct 2017Samuel L. SmithQuoc V. Le

We consider two questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well? Our work responds to Zhang et al. (2016), who showed deep neural networks can easily memorize randomly labeled training data, despite generalizing well on real labels of the same inputs... (read more)

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