Three learning stages and accuracy-efficiency tradeoff of restricted Boltzmann machines.

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

SUBMITTER: Dabelow L 

PROVIDER: S-EPMC9482660 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature