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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

November 1994, Vol. 6, No. 6, Pages 1174-1184
(doi: 10.1162/neco.1994.6.6.1174)
© 1994 Massachusetts Institute of Technology
Learning in Boltzmann Trees
Article PDF (499.13 KB)
Abstract

We introduce a large family of Boltzmann machines that can be trained by standard gradient descent. The networks can have one or more layers of hidden units, with tree-like connectivity. We show how to implement the supervised learning algorithm for these Boltzmann machines exactly, without resort to simulated or mean-field annealing. The stochastic averages that yield the gradients in weight space are computed by the technique of decimation. We present results on the problems of N-bit parity and the detection of hidden symmetries.