Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey
It serves as an educational resource for researchers and practitioners in fields like data science and neural computation, but it is incremental as it synthesizes existing knowledge without new contributions.
This paper provides a tutorial and survey on Boltzmann Machines, Restricted Boltzmann Machines, and Deep Belief Networks, covering their structures, training methods, and applications.
This is a tutorial and survey paper on Boltzmann Machine (BM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We start with the required background on probabilistic graphical models, Markov random field, Gibbs sampling, statistical physics, Ising model, and the Hopfield network. Then, we introduce the structures of BM and RBM. The conditional distributions of visible and hidden variables, Gibbs sampling in RBM for generating variables, training BM and RBM by maximum likelihood estimation, and contrastive divergence are explained. Then, we discuss different possible discrete and continuous distributions for the variables. We introduce conditional RBM and how it is trained. Finally, we explain deep belief network as a stack of RBM models. This paper on Boltzmann machines can be useful in various fields including data science, statistics, neural computation, and statistical physics.