Simulated tempering and mean field annealing for mapping to multicomputers. (c1996)

1996 ◽  
Author(s):  
Gabriel I. Aghazarian
Keyword(s):  
Mean Field ◽  
Simulated Tempering ◽  
Field Annealing

Optimum displacement estimates using mean field annealing

1993 ◽  
Author(s):  
Ikhlas M. Abdelqader ◽  
Sarah A. Rajala ◽  
Griff L. Bilbro ◽  
Wesley E. Snyder
Keyword(s):  
Mean Field ◽  
Field Annealing

Learning in Boltzmann Trees

1994 ◽  
Vol 6 (6) ◽  
pp. 1174-1184 ◽  
Author(s):  
Lawrence Saul ◽  
Michael I. Jordan
Keyword(s):  
Supervised Learning ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Mean Field ◽  
Large Family ◽  
Weight Space ◽  
Hidden Symmetries ◽  
Boltzmann Machines ◽  
Field Annealing

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.

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