nips nips2013 nips2013-312 nips2013-312-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Sam Patterson, Yee Whye Teh
Abstract: 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 to large scale data. We apply this method to latent Dirichlet allocation in an online minibatch setting, and demonstrate that it achieves substantial performance improvements over the state of the art online variational Bayesian methods. 1
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