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Poster
On the Implicit Bias of Dropout
Poorya Mianjy · Raman Arora · Rene Vidal
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.
Author Information
Poorya Mianjy (Johns Hopkins University)
Raman Arora (Johns Hopkins University)
Rene Vidal (Johns Hopkins University)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: On the Implicit Bias of Dropout »
Fri Jul 13th 03:30 -- 03:40 PM Room K11
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