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Abstract: . It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of training cases to be approximated arbitrarily closely, in contrast to previous approaches which approximate the posterior weight distribution by a Gaussian. In this work, the Hybrid Monte Carlo method is implemented in conjunction with simulated annealing, in order to speed... (Update)
Context of citations to this paper: More
...networks is also critical; it permits the development of hybrid systems that have comparable or improved convergence properties. [13] A chief purpose for studying random sampling algorithms that can emulate neural network learning is to develop efficient,...
...from the sin(2x) x function in the interval [0. 001, 2#] Six chains of 6000 samples were generated using Hybrid Monte Carlo sampling [11], and the first 500 samples of each chain were discarded. A SOM was computed of the combined data from all the chains. The Fisher matrix...
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7: Bayesian learning for neural networks (context) - Neal - 1995
6: Bayesian back-propagation (context) - Buntine, Weigend - 1991
5: Hybrid Monte Carlo (context) - Duane, Kennedy et al. - 1987
BibTeX entry: (Update)
Neal, R. 1993. Bayesian training of backpropagation networks by the hybrid Monte Carlo method. Tech. Rep. Dpt. Comp. Sci., U. Toronto. http://citeseer.ist.psu.edu/neal93bayesian.html More
@techreport{ neal92bayesian,
author = "Radford M. Neal",
title = "Bayesian Training of Backpropagation Networks by the Hybrid {M}onte {C}arlo Method",
number = "CRG-TR-92-1",
year = "1992",
url = "citeseer.ist.psu.edu/neal93bayesian.html" }
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Documents on the same site (http://www.cs.toronto.edu/~radford/papers-online.html): More
Monte Carlo Implementation of Gaussian Process Models for Bayesian .. - Neal (1997) (Correct)
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