An Efficient Learning Procedure for Deep Boltzmann Machines
We present a new learning algorithm for Boltzmann Machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The u...
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2010
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| Online Access: | http://hdl.handle.net/1721.1/57474 |