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.
only if” direction is immediate, sinceM sim- ulates all spin systems. Simulating a flag system with high enough cut-off yields a flag system itself. We prove the “if
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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.