Exploration of the generative capabilities of Boltzmann machines applied to social systems under the majority rule
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Computer Science > Machine Learning
Title:Exploration of the generative capabilities of Boltzmann machines applied to social systems under the majority rule
Abstract:We study the generative capabilities of Boltzmann machines to recover systems governed by the majority rule under critical conditions. To this end, we train deep belief networks (DBNs) with different configurations, where the first layer can use Gaussian visible units with more than two states (i.e., non-binary units). We then allow the DBN to "dream" samples conditioned on visible units that we keep fixed, and we measure the deviation of this dreamed system from the real one. We also corroborate, using a discrete thermometer based on a convolutional network, that the reconstructions remain in a critical state. Across several training sessions with different architectures, we show that, despite the complexity of the problem, the DBN can recover samples that remain critical even under input noise, with a gradual degradation of physical observables relative to the original sample.
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| Cite as: | arXiv:2607.23349 [cs.LG] |
| (or arXiv:2607.23349v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.23349
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Gonzalo A. Ruz Ph.D. [view email][v1] Sat, 25 Jul 2026 19:55:16 UTC (1,952 KB)
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