The Wake-Sleep Algorithm for Unsupervised
Neural Networks.
Geoff Hinton Peter Dayan Brendan Frey
Radford Neal
Science, 268, 1158-1160.
Abstract
An unsupervised learning algorithm for a multilayer network of
stochastic neurons is described. Bottom-up "recognition" connections
convert the input into representations in successive hidden layers,
and top-down "generative" connections reconstruct the representation
in one layer from the representation in the layer above. In the "wake"
phase, neurons are driven by recognition connections, and generative
connections are adapted to increase the probability that they would
reconstruct the correct activity vector in the layer below. In the
"sleep" phase, neurons are driven by generative connections, and
recognition connections are adapted to increase the probability that
they would produce the correct activity vector in the layer above.
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