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  The Helmholtz Machine Through Time

Hinton, G., Dayan, P., To, A., & Neal, R. (1995). The Helmholtz Machine Through Time. In F. Fogelman-Soulie, & R. Gallinari (Eds.), Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN 1995) (pp. 483-490). Paris, France: RC2.

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 Creators:
Hinton, GE, Author
Dayan, P1, Author              
To, A, Author
Neal, RM, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: We describe the "wake-sleep'' algorithm that allows a multilayer, unsupervised, stochastic neural network to build a hierarchical, top-down generative model of an ensemble of data vectors. Because the generative model uses distributed representations that are a non-linear function of the input, it is intractable to compute the posterior probability distribution over hidden representations given the generative model and the current data vector. It is therefore intractable to fit the generative model to data using standard techniques such as gradient descent or EM. Instead of computing the posterior distribution exactly, a "Helmholtz Machine'' uses a separate set of bottom-up "recognition'' connections to produce a compact approximation to the posterior distribution. The wake-sleep algorithm uses the top-down generative connections to provide training data for the bottom-up recognition connections and vice versa. In this paper, we show that the wake-sleep algorithm can be generalized to model the temporal structure in sequences of data vectors. This gives a very simple online algorithm that fits generative models which have distributed hidden representations which can be exponentially more powerful than conventional Hidden Markov Models.

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 Dates: 1995-10
 Publication Status: Published in print
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Title: Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN 1995)
Place of Event: Paris, France
Start-/End Date: 1995-10-09 - 1995-10-13

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Title: Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN 1995)
Source Genre: Proceedings
 Creator(s):
Fogelman-Soulie, F, Editor
Gallinari, R, Editor
Affiliations:
-
Publ. Info: Paris, France : RC2
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 483 - 490 Identifier: ISBN: 2-910085-18-X