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

NeurIPS 2016 Matthew J. JohnsonDavid DuvenaudAlexander B. WiltschkoSandeep R. DattaRyan P. Adams

We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models... (read more)

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