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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.|
|Citation:||Mimno, David, Matthew D. Hoffman, David M. Blei. "Sparse stochastic inference for latent Dirichlet allocation." ICML'12: Proceedings of the 29th International Conference on International Conference on Machine Learning: pp. 1515–1522.|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||ICML'12: Proceedings of the 29th International Conference on Machine Learning|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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