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Stochastic variational inference and its derivatives in the form ofvariational autoencoders enjoy the ability to perform Bayesian inference onlarge datasets in an efficient manner. However, performing inference with a VAErequires a certain design choice (i.e. reparameterization trick) to allowunbiased and low variance gradient estimation, restricting the types of modelsthat can be created. To overcome this challenge, an alternative estimator basedon natural evolution strategies is proposed. This estimator does not makeassumptions about the kind of distributions used, allowing for the creation ofmodels that would otherwise not have been possible under the VAE framework.

Natural Evolution Strategies as a Black Box Estimator for Stochastic Variational Inference