Universal spin models are universal approximators of probability distributions, yielding a unified recipe for universal approximation theorems in models such as restricted Boltzmann machines and deep belief networks.
Boltzmann machines and energy-based models
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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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cond-mat.dis-nn 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Universal Spin Models are Universal Approximators in Machine Learning
Universal spin models are universal approximators of probability distributions, yielding a unified recipe for universal approximation theorems in models such as restricted Boltzmann machines and deep belief networks.