An empirical analysis of dropout in piecewise linear networks

21 Dec 2013David Warde-FarleyIan J. GoodfellowAaron CourvilleYoshua Bengio

The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function... (read more)

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