To solve these scaling issues , we implement Online Variational Bayesian Inference ( Hoffman et al. , 2010 ; Hoffman et al. , 2012 ) for our models .
stochastic variational inference arxiv eprints
Matthew Hoffman David M Blei Chong Wang John Paisley
Prior work using mLDA has used Gibbs Sampling to approximate the posterior , but we found this method did not scale with larger values of K , especially when applied to the relatively large deWaC corpus . To solve these scaling issues , we implement Online Variational Bayesian Inference ( Hoffman et al. , 2010 ; Hoffman et al. , 2012 ) for our models . In Variational Bayesian Inference ( VBI ) , one approximates the true posterior using simpler distributions with free variables .
To solve these scaling issues , we implement Online Variational Bayesian Inference ( Hoffman et al. , 2010 ; @@CITATION ) for our models .