Paper
Does the Wake-sleep Algorithm Produce Good Density Estimators?
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. COMPETITORSWe compare the wake-sleep algorithm with six other density estimation methods.All data units are binary and can take on values d k = 1 (on) and d k = 0 (off).Gzip.Gzip (Gailly, 1993) is a practical compression method based on Lempel-Ziv coding.This sequential data compression technique encodes future segments of data by transmit-
Authors: Brendan J. Frey · Geoffrey E. Hinton · Peter Dayan