Transcription of Dropout as a Bayesian Approximation: Representing Model ...
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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.
We de-note by y i the observed output corresponding to input x i for 1 i Ndata points, and the input and output sets as X;Y. During NN optimisation a regularisation term is often added. We often use L 2 regularisation weighted by some weight decay , resulting in a minimisation objective (often referred to as cost), L dropout:= 1 N XN i=1 E(y i ...
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