Search results with tag "Representing model uncertainty in deep learning"

Representing Model Uncertainty in Deep Learning - arXiv

3. Dropout as a Bayesian Approximation 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 the probabilistic deep Gaussian process (Damianou & Lawrence,2013) (marginalised over its covariance function parameters).

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