Distilling the Posterior in Bayesian Neural Networks

ICML 2018 Kuan-Chieh WangPaul VicolJames LucasLi GuRoger GrosseRichard Zemel

Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing samples from the BNN posterior using mini-batches... (read more)

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