Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation

Abstract

There has been an explosion in the amount of digital text information available in recent years, leading to challenges of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on very large-scale corpora… (More)
DOI: 10.1145/2487575.2487697

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@inproceedings{Foulds2013StochasticCV, title={Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation}, author={James R. Foulds and Levi Boyles and Christopher DuBois and Padhraic Smyth and Max Welling}, booktitle={KDD}, year={2013} }