Multi-Prediction Deep Boltzmann Machines
نویسندگان
چکیده
We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MPDBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1
منابع مشابه
Foundations and Advances in Deep Learning
Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Kyunghyun Cho Name of the doctoral dissertation Foundations and Advances in Deep Learning Publisher Unit Department of Information and Computer Science Series Aalto University publication series DOCTORAL DISSERTATIONS 21/2014 Field of research Machine Learning Manuscript submitted 2 September 2013 Date of the defence 21 March ...
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