Dabelow L, Ueda M. Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines.
Nat Commun 2022;
13:5474. [PMID:
36115845 PMCID:
PMC9482660 DOI:
10.1038/s41467-022-33126-x]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 09/01/2022] [Indexed: 11/15/2022] Open
Abstract
Restricted Boltzmann Machines (RBMs) offer a versatile architecture for unsupervised machine learning that can in principle approximate any target probability distribution with arbitrary accuracy. However, the RBM model is usually not directly accessible due to its computational complexity, and Markov-chain sampling is invoked to analyze the learned probability distribution. For training and eventual applications, it is thus desirable to have a sampler that is both accurate and efficient. We highlight that these two goals generally compete with each other and cannot be achieved simultaneously. More specifically, we identify and quantitatively characterize three regimes of RBM learning: independent learning, where the accuracy improves without losing efficiency; correlation learning, where higher accuracy entails lower efficiency; and degradation, where both accuracy and efficiency no longer improve or even deteriorate. These findings are based on numerical experiments and heuristic arguments.
Restricted Boltzmann Machines are unsupervised machine learning model that have been applied for various tasks from image analysis to many-body physics. The authors elaborate the interplay of accuracy and efficiency of this model and define possible balance regimes for applications.
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