Generalising the Discriminative Restricted Boltzmann Machine.

ICANN(2017)

引用 13|浏览50
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摘要
We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the ({0, 1})-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This paper shows that this function can be extended to the Binomial and ({-1,+1})-Bernoulli hidden units.
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关键词
Restricted Boltzmann Machine, Discriminative learning, Hidden layer activation function
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