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• W122577237 abstract "A deep Boltzmann machine (DBM) is a recently introduced Markov random field model that has multiple layers of hidden units. It has been shown empirically that it is difficult to train a DBM with approximate maximum-likelihood learning using the stochastic gradient unlike its simpler special case, restricted Boltzmann machine (RBM). In this paper, we propose a novel pretraining algorithm that consists of two stages; obtaining approximate posterior distributions over hidden units from a simpler model and maximizing the variational lower-bound given the fixed hidden posterior distributions. We show empirically that the proposed method overcomes the difficulty in training DBMs from randomly initialized parameters and results in a better, or comparable, generative model when compared to the conventional pretraining algorithm." @default.
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• W122577237 date "2015-01-01" @default.
• W122577237 modified "2023-09-26" @default.
• W122577237 title "How to Pretrain Deep Boltzmann Machines in Two Stages" @default.
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• W122577237 doi "https://doi.org/10.1007/978-3-319-09903-3_10" @default.
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