Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines. (arXiv:2209.00873v1 [cs.LG])

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 …

accuracy arxiv boltzmann efficiency machines