Boltzmann machines and energy-based models

Takayuki Osogami

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arXiv · 1708.06008

Boltzmann machines and energy-based models

Takayuki Osogami

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Abstract

We review Boltzmann machines and energy-based models. A Boltzmann machine defines a probability distribution over binary-valued patterns. One can learn parameters of a Boltzmann machine via gradient based approaches in a way that log likelihood of data is increased. The gradient and Hessian of a Boltzmann machine admit beautiful mathematical representations, although computing them is in general intractable. This intractability motivates approximate methods, including Gibbs sampler and contrastive divergence, and tractable alternatives, namely energy-based models.

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Takayuki Osogami. 2017-08-20. Boltzmann machines and energy-based models. https://arxiv.org/abs/1708.06008

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