Sparse stochastic inference for latent dirichlet allocation

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Sparse stochastic inference for latent dirichlet allocation (2012)

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by David Mimno , Matthew D. Hoffman , David M. Blei

Venue:	In International Conference on Machine Learning
Citations:	43 - 4 self

BibTeX

@INPROCEEDINGS{Mimno12sparsestochastic,
    author = {David Mimno and Matthew D. Hoffman and David M. Blei},
    title = {Sparse stochastic inference for latent dirichlet allocation},
    booktitle = {In International Conference on Machine Learning},
    year = {2012}
}

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Abstract

We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models. 1.

Keyphrases

latent dirichlet allocation sparse stochastic inference bayesian topic model variational inference online stochastic inference sparse gibbs many bayesian hidden-variable model hybrid algorithm

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