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Dropout as a Bayesian Approximation: Representing Model ...
Dropout as a Bayesian Approximation: Representing Model uncertainty in Deep LearningYarin of CambridgeAbstractDeep learning tools have gained tremendous at-tention in applied machine learning. Howeversuch tools for regression and classification donot capture Model compari-son, Bayesian models offer a mathematicallygrounded framework to reason about Model un-certainty, but usually come with a prohibitivecomputational cost. In this paper we develop anew theoretical framework casting Dropout train-ing in deep neural networks (NNs) as approxi-mate Bayesian inference in deep Gaussian pro-cesses. A direct result of this theory gives ustools to Model uncertainty with Dropout NNs extracting information from existing models thathas been thrown away so far.
turing, to name a few (Baldi et al.,2014;Anjos et al.,2015; Bergmann et al.,2014). Tools such as neural networks (NNs), dropout, convolutional neural networks (convnets), and others are used extensively. However, these are fields in which representing model uncertainty is of crucial impor-tance (Krzywinski & Altman,2013;Ghahramani,2015).
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