Learning Structural Weight Uncertainty for Sequential Decision-Making

30 Dec 2017 Ruiyi Zhang Chunyuan Li Changyou Chen Lawrence Carin

Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications. Bayesian methods, such as Stein variational gradient descent (SVGD), offer an elegant framework to reason about NN model uncertainty... (read more)

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