Transcription of Dropout as a Bayesian Approximation: Representing Model ...
{{id}} {{{paragraph}}}
Example: bankruptcy
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. This mitigatesthe problem of Representing uncertainty in deeplearning without sacrificing either computationalcomplexity or test accuracy.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (a) Arbitrary function f(x) as a function of data x (softmax input) (b) ˙(f(x)) as a function of data x (softmax output)
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: