Statistics > Machine Learning
[Submitted on 16 Jan 2013 (v1), last revised 1 May 2013 (this version, v3)]
Title:Joint Training Deep Boltzmann Machines for Classification
View PDFAbstract:We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model 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 training procedure called multi-prediction training. The resulting model can either be interpreted as a single generative model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent networks that share parameters and may be approximately averaged together using a novel technique we call the multi-inference trick. We show that our approach performs competitively for classification and outperforms previous methods in terms of accuracy of approximate inference and classification with missing inputs.
Submission history
From: Ian Goodfellow [view email][v1] Wed, 16 Jan 2013 03:21:27 UTC (116 KB)
[v2] Wed, 13 Mar 2013 18:43:00 UTC (116 KB)
[v3] Wed, 1 May 2013 04:48:20 UTC (395 KB)
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