Practical Deep Learning with Bayesian Principles

NeurIPS 2019 Kazuki OsawaSiddharth SwaroopAnirudh JainRuna EschenhagenRichard E. TurnerRio YokotaMohammad Emtiyaz Khan

Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference... (read more)

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