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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. This mitigatesthe problem of Representing uncertainty in deeplearning without sacrificing either computationalcomplexity or test accuracy. We perform an ex-tensive study of the properties of Dropout s un-certainty.
3. Dropout as a Bayesian Approximation We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to the probabilistic deep Gaussian process (Damianou & Lawrence,2013) (marginalised over its covariance function parameters).
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