Joint Training Deep Boltzmann Machines for Classification

Ian Goodfellow, Aaron Courville, Yoshua Bengio

Invalid Date (modified: Jan 17, 2013) ICLR 2013 conference submission readers: everyone
  • Decision: conferenceOral-iclr2013-workshop
  • Abstract: We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classification tasks. In our approach, we train all layers of the DBM simultaneously, using a novel inpainting-based objective function that facilitates second order optimization and line searches.
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