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Does the Wake-Sleep Algorithm Produce Good Density Estimators?

Brendan J. Frey, Geoffrey E. Hinton
and Peter Dayan
Department of Computer Science
University of Toronto


The wake-sleep algorithm (Hinton, Dayan, Frey and Neal 1995) is a relatively efficient method of fitting a multilayer stochastic generative model to high-dimensional data. In addition to the top-down connections in the generative model, it makes use of bottom-up connections for approximating the probability distribution over the hidden units given the data, and it trains these bottom-up connections using a simple delta rule. We use a variety of synthetic and real data sets to compare the performance of the wake-sleep algorithm with Monte Carlo and mean field methods for fitting the same generative model and also compare it with other models that are less powerful but easier to fit.

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In Advances in Neural Information Processing Systems 8. MIT Press (1996): Cambridge, MA.
Presented at the Neural Information Processing Systems Conference, Denver, Colorado, Dec. 1995.

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