We propose a general modeling and inference framework that combines the complementary strengths of probabilistic graphical models and deep learning methods. Our model family composes latent graphical models with neural network observation likelihoods. For inference, we use recognition networks to produce local evidence potentials, then combine them with the model distribution using efficient message-passing algorithms. All components are trained simultaneously with a single stochastic variational inference objective. We illustrate this framework by automatically segmenting and categorizing mouse behavior from raw depth video, and demonstrate several other example models.
REF combined a structured graphical model in latent space with a deep generative network, where an inference network produces local evidence potentials for the message passing algorithms.
Composing graphical models with neural networks for structured representations and fast inference
NIPS
Computer Science Mathematics