On the Implicit Bias of Dropout

Poorya Mianjy; Raman Arora; Rene Vidal

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arXiv · 1806.09777

On the Implicit Bias of Dropout

Poorya Mianjy·Raman Arora·Rene Vidal

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Abstract

Algorithmic approaches endow deep learning systems with implicit bias that helps them generalize even in over-parametrized settings. In this paper, we focus on understanding such a bias induced in learning through dropout, a popular technique to avoid overfitting in deep learning. For single hidden-layer linear neural networks, we show that dropout tends to make the norm of incoming/outgoing weight vectors of all the hidden nodes equal. In addition, we provide a complete characterization of the optimization landscape induced by dropout.

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Poorya Mianjy, Raman Arora, Rene Vidal. 2018-06-26. On the Implicit Bias of Dropout. https://arxiv.org/abs/1806.09777

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