(Enter summary)
Abstract: We develop a refined mean field approximation for inference and
learning in probabilistic neural networks. Our mean field theory,
unlike most, does not assume that the units behave as independent
degrees of freedom; instead, it exploits in a principled way the
existence of large substructures that are computationally tractable.
To illustrate the advantages of this framework, we show how to
incorporate weak higher order interactions into a first-order hidden
Markov model, treating the... (Update)
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BibTeX entry: (Update)
L.K. Saul and M.I. Jordan. Exploiting tractable substructure in intractable networks. In Proc. NIPS, 1995. http://citeseer.ist.psu.edu/saul95exploiting.html More
@inproceedings{ saul96exploiting,
author = "Lawrence K. Saul and Michael I. Jordan",
title = "Exploiting Tractable Substructures in Intractable Networks",
booktitle = "Advances in Neural Information Processing Systems",
volume = "8",
publisher = "The {MIT} Press",
editor = "David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo",
pages = "486--492",
year = "1996",
url = "citeseer.ist.psu.edu/saul95exploiting.html" }
Citations (may not include all citations):
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The Gibbs machine applied to hidden Markov model problems (context) - Luttrell - 1989
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Royal Signals and Radar Establishment: SP Research Note (context) - Signals, SP et al. - 1988
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Mean field networks that learn to discriminate temporally di.. (context) - Hunter, Proc et al. - 1990
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