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.
art methods. Lastly we give a quantitative assessment of model uncertainty in the setting of reinforcement learning, on a practical task similar to that used in deep reinforce-ment learning (Mnih et al.,2015).1 2. Related Research It has long been known that infinitely wide (single hid-den layer) NNs with distributions placed over their weights
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