Implicit Weight Uncertainty in Neural Networks

11/03/2017 ∙ by Nick Pawlowski, et al. ∙ 0

We interpret HyperNetworks within the framework of variational inference within implicit distributions. Our method, Bayes by Hypernet, is able to model a richer variational distribution than previous methods. Experiments show that it achieves comparable predictive performance on the MNIST classification task while providing higher predictive uncertainties compared to MC-Dropout and regular maximum likelihood training.

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