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Assessing high-dimensional latent variable models

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If you have a question about this talk, please contact Zoubin Ghahramani .

Having built a probabilistic model, a natural question is: “what probability does my model assign to the data?”.

We might fit the model’s parameters to avoid having to compute an intractable marginal likelihood. Even then, evaluating a test-set probability with fixed parameters can be difficult. I will discuss recent work on evaluating high-dimensional undirected graphical models and models with many latent variables. This allows direct comparisons of the probabilistic predictions made by graphical models with hundreds of thousands of parameters against simpler alternatives.

Details: I will review Restricted Boltzmann Machines (RBM’s) and Annealed Importance Sampling (AIS). Then I will present new work that allows efficient assessment of more general Boltzmann machines and Deep Belief Networks.

This talk is part of the Machine Learning @ CUED series.

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