Sparse stochastic inference for latent Dirichlet allocation
Author(s): Mimno, David; Hoffman, Matthew D; Blei, David M
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Mimno, David | - |
| dc.contributor.author | Hoffman, Matthew D | - |
| dc.contributor.author | Blei, David M | - |
| dc.date.accessioned | 2021-10-08T19:44:13Z | - |
| dc.date.available | 2021-10-08T19:44:13Z | - |
| dc.date.issued | 2012-06 | en_US |
| dc.identifier.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. | en_US |
| dc.identifier.uri | https://dl.acm.org/doi/10.5555/3042573.3042767 | - |
| dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr1352g | - |
| dc.description.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. | en_US |
| dc.format.extent | 1515–1522 | en_US |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartof | ICML'12: Proceedings of the 29th International Conference on Machine Learning | en_US |
| dc.relation.ispartofseries | ICML'12; | - |
| dc.rights | Final published version. Article is made available in OAR by the publisher's permission or policy. | en_US |
| dc.title | Sparse stochastic inference for latent Dirichlet allocation | en_US |
| dc.type | Conference Article | en_US |
| pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceeding | en_US |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| SparseStochasticInferenceLDA.pdf | 686 kB | Adobe PDF | View/Download |
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