University of Montreal
Joint Training Deep Boltzmann Machines for Classification
Wednesday 12th of December 2012 at 12:00pm
The traditional deep Boltzmann machine training algorithm requires a greedy layerwise pretraining
phase. Existing techniques for avoiding greedy pretraining do not perform as well for classification as the
layerwise method. I show that 2nd order methods applied to a deterministic training criterion can obtain
better classification performance than the existing joint training methods.
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