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NIPS
2008

Evaluating probabilities under high-dimensional latent variable models

11 years 1 months ago
Evaluating probabilities under high-dimensional latent variable models
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. While the method is based on Markov chains, estimates based on short runs are formally unbiased. In expectation, the log probability of a test set will be underestimated, and this could form the basis of a probabilistic bound. The method is much cheaper than gold-standard annealing-based methods and only slightly more expensive than the cheapest Monte Carlo methods. We give examples of the new method substantially improving simple variational bounds at modest extra cost.
Iain Murray, Ruslan Salakhutdinov
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Iain Murray, Ruslan Salakhutdinov
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