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Dropout as a Bayesian Approximation: Appendix | | Yarin Gal
; Zoubin Ghahramani
; | | Date: |
6 Jun 2015 | | Abstract: | We show that a multilayer perceptron (MLP) with arbitrary depth and
nonlinearities, with dropout applied after every weight layer, is
mathematically equivalent to an approximation to a well known Bayesian model.
This interpretation offers an explanation to some of dropout’s key properties,
such as its robustness to over-fitting. Our interpretation allows us to reason
about uncertainty in deep learning, and allows the introduction of the Bayesian
machinery into existing deep learning frameworks in a principled way.
This document is an appendix for the main paper "Dropout as a Bayesian
Approximation: Representing Model Uncertainty in Deep Learning" by Gal and
Ghahramani, 2015. | | Source: | arXiv, 1506.2157 | | Services: | Forum | Review | PDF | Favorites | |
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