Deep belief networks are exact

arXiv9 septembrie 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.05572v1 Announce Type: cross Abstract: We prove that every strictly positive probability distribution on \(\{-1,1\}^n\) is represented exactly by a sigmoid belief network with finite parameters. This answers a question of Sutskever and Hinton. The proof upgrades their probability-sharing approximation to exact representation using Brouwer's fixed-point theorem.

Abstract:We prove that every strictly positive probability distribution on \(\{-1,1\}^n\) is represented exactly by a sigmoid belief network with finite parameters. This answers a question of Sutskever and Hinton. The proof upgrades their probability-sharing approximation to exact representation using Brouwer's fixed-point theorem.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Probability (math.PR)

arXiv:2609.05572 [cs.AI]

(or arXiv:2609.05572v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.05572 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Deep belief networks are exact

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