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We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment the approximate posterior in a variational setting for Bayesian neural networks.
A mean field theory learning algorithm for neural networks
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Markov chain monte carlo and variational inference: Bridging the gap
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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Dinh, Laurent, Sohl-Dickstein, Jascha, and Bengio, Samy · 2016
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Variational Dropout Sparsifies Deep Neural Networks
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