On Convergence and Generalization of Dropout Training

Poorya Mianjy; Raman Arora

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arXiv · 2010.12711

On Convergence and Generalization of Dropout Training

Poorya Mianjy·Raman Arora

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Abstract

We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the data distribution with a positive margin, we show that dropout training with logistic loss achieves $\epsilon$-suboptimality in test error in $O(1/\epsilon)$ iterations.

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Poorya Mianjy, Raman Arora. 2020-10-23. On Convergence and Generalization of Dropout Training. https://arxiv.org/abs/2010.12711

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