Dropout Training as Adaptive Regularization

NIPS, pp. 351-359, 2013.

Cited by: 397|Bibtex|Views60
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is first-order equivalent to an L2 regularizer applied after scaling the features by an ...More

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