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Colloquium - Hinton

ECCR 265

Fitting Intractable Generative Models
Department of Computer Science, University of Toronto

When fitting a stochastic generative model to data it is normal to compute the posterior probability distribution that is induced over configurations of the hidden variables by each observation. For simple models such as mixtures of Gaussians and factor analysis this posterior distribution can be computed exactly. For more interesting generative models composed of multiple layers of non-linear units it is intractable to compute the posterior distribution. I shall describe various ways of approximating the posterior distribution and show that simple learning rules can improve the generative model even when the approximations are poor.

Refreshments will be served immediately before the talk at 3:30pm.
Hosted by Michael Mozer.

The Department holds colloquia throughout the Fall and Spring semesters. These colloquia, open to the public, are typically held on Thursday afternoons, but sometimes occur at other times as well. If you would like to receive email notification of upcoming colloquia, subscribe to our Colloquia Mailing List. If you would like to schedule a colloquium, see Colloquium Scheduling.

Sign language interpreters are available upon request. Please contact Stephanie Morris at least five days prior to the colloquium.

See also:
Department of Computer Science
College of Engineering and Applied Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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