Composing graphical models with neural networks

(most slides stolen from Matthew James Johnson)

Outline● Motivation and Examples● Stochastic Variational Inference● Structured Variational Auto-Encoders

Example: Generative Clustering

DataGMM

Example: Generative Clustering

Application: Parse mouse behaviour

Aim: Generative model for different mouse behaviour

Application: Parse mouse behaviour

Input: Videos

Aim: Come up with a generative model for the behaviour and the video frames

Example: Switching Linear Dynamical System

Special Cases

GMM LDS

Stochastic Variational Inference

Mean Field Approximation

Objective function

Equivalent to the Kalman Filter

Algorithm

Structured Variational Auto Encoder

Mean Field Approximation

Objective function

Variational Auto-Encoder

Algorithm

Conclusion

Possible applicationsReinforcement Learning and BanditsSemi-supervised Learning

Composing graphical models with neural networks · — more flexible models can require slow...

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