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Recent Developments in Bayesian Deep Learning

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If you have a question about this talk, please contact Robert Pinsler.

Different groups have taken differing positions on the motivations and goals of Bayesian deep learning. This has led to a range of ideas on which benchmarks we should use and the importance/quality of the prior and of posterior inference. We hope to start a discussion on these topics. In addition, we provide an overview of state-of-the-art techniques in BDL , with an emphasis on Stochastic Gradient MCMC and variational inference.

This talk is part of the Machine Learning Reading Group @ CUED series.

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