The effect of priors on Learning with Restricted Boltzmann Machines

Gianluca Manzan and Daniele Tantari

Physica A: Statistical Mechanics and its Applications, 2025, vol. 674, issue C

Abstract: Restricted Boltzmann Machines (RBMs) are generative models designed to learn from data with a rich underlying structure. In this work, we explore a teacher–student setting where a student RBM learns from examples generated by a teacher RBM, with a focus on the effect of the unit priors on learning efficiency. We consider a parametric class of priors that interpolate between continuous (Gaussian) and binary variables. This approach models various possible choices of visible units, hidden units, and weights for both the teacher and student RBMs.

Keywords: Statistical mechanics; Machine learning; Self-supervised learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125004182

DOI: 10.1016/j.physa.2025.130766

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