Learning Algorithms for Restricted Boltzmann Machines

Speaker: Devin Willmott, University of Kentucky

Abstract: Restricted Boltzmann machines (RBMs) have played a central role in the development of deep learning. In this talk, we will introduce the theoretical framework behind stochastic binary RBMs, give motivation and a derivation for the most commonly used RBM learning algorithm (contrastive divergence), and prove some analytic results related to its convergence properties.