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We propose a general modeling and inference framework that composesprobabilistic graphical models with deep learning methods and combines theirrespective strengths. Our model family augments graphical structure in latentvariables with neural network observation models. For inference, we extendvariational autoencoders to use graphical model approximating distributionswith recognition networks that output conjugate potentials. All components ofthese models are learned simultaneously with a single objective, giving ascalable algorithm that leverages stochastic variational inference, naturalgradients, graphical model message passing, and the reparameterization trick.We illustrate this framework with several example models and an application tomouse behavioral phenotyping.

Composing graphical models with neural networks for structured representations and fast inference