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Monday, September 10, 2012
11:00 AM - 12:00 PM
CNLS Conference Room (TA-3, Bldg 1690)


Parameter Estimation in Intractable Probabilistic Models by Minimum Probability Flow Learning

Jascha Sohl-Dickstein
Stanford University

Fitting probabilistic models to data is often extremely difficult, due to the general intractability of the partition function. We propose a new parameter fitting method which bypasses this difficulty by considering only small perturbations from the data distribution toward the model distribution. Parameter estimation using this method is demonstrated for several probabilistic models, including a product-of-experts model of natural images, and an Ising spin glass where it outperforms current techniques by at least an order of magnitude in convergence time with lower error in the recovered coupling parameters. The application of this method to pattern storage in a Hopfield associative memory is also discussed.

Host: Peter Loxley, T-5, 665-3203