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Dropout as a Bayesian Approximation: Appendix

(2015)

Cited: 69|Views40
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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 a well known Bayesian model. This interpretation might offer an explanation to some of dropout's key properties, such as its robustness to over-fitting. Our interpret...More

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