Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
published: Nov. 7, 2014, recorded: January 2014, views: 3615
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied online. We apply this method to latent Dirichlet allocation in an online setting, and demonstrate that it achieves substantial performance improvements to the state of the art online variational Bayesian methods.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !