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Bayesian inference plays an important role in advancing machine learning, butfaces computational challenges when applied to complex models such as deepneural networks. Variational inference circumvents these challenges byformulating Bayesian inference as an optimization problem and solving it usinggradient-based optimization. In this paper, we argue in favor ofnatural-gradient approaches which, unlike their gradient-based counterparts,can improve convergence by exploiting the information geometry of thesolutions. We show how to derive fast yet simple natural-gradient updates byusing a duality associated with exponential-family distributions. An attractivefeature of these methods is that, by using natural-gradients, they are able toextract accurate local approximations for individual model components. Wesummarize recent results for Bayesian deep learning showing the superiority ofnatural-gradient approaches over their gradient counterparts.

Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models