Scopus

A Fast Algorithm for Posterior Inference with Latent Dirichlet Allocation

Năm XB 2018 Tạp chí / Hội thảo Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 10752 LNAI DOI / Link https://doi.org/10.1007/978-3-319-75420-8_13 ↗

Tác giả

Tài liệu tham khảo

[1] Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

[2] Hazan, E., Kale, S.: Projection-free online learning. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 26 June – 1 July 2012, Edinburgh, Scotland, UK (2012)

[3] Than, K., Doan, T.: Dual online inference for latent Dirichlet allocation. In: ACML (2014)

[4] Than, K., Doan, T.: Guaranteed inference in topic models. arXiv preprint arXiv:1512.03308 (2015)

[5] Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

[6] Falush, D., Stephens, M., Pritchard, J.K.: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164(4), 1567 (2003)

[7] Hoffman, M., Blei, D.M., Mimno, D.M.: Sparse stochastic inference for latent Dirichlet allocation. In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), pp. 1599–1606. ACM (2012)

[8] Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 937–946. ACM (2009)

[9] Grimmer, J.: A Bayesian hierarchical topic model for political texts: measuring expressed agendas in senate press releases. Polit. Anal. 18(1), 1–35 (2010)

[10] Schwartz, H.A., Eichstaedt, J.C., Dziurzynski, L., Kern, M.L., Blanco, E., Kosinski, M., Stillwell, D., Seligman, M.E., Ungar, L.H.: Toward personality insights from language exploration in social media. In: AAAI Spring Symposium: Analyzing Microtext (2013)

[11] Teh, Y.W., Kurihara, K., Welling, M.: Collapsed variational inference for HDP. In: Advances in Neural Information Processing Systems, pp. 1481–1488 (2007)

[12] Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 27–34. AUAI Press (2009)

[13] Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(suppl 1), 5228–5235 (2004)

[14] Sontag, D., Roy, D.: Complexity of inference in latent Dirichlet allocation. In: Neural Information Processing System (NIPS) (2011)

[15] Dang, C.D., Lan, G.: Stochastic block mirror descent methods for nonsmooth and stochastic optimization. SIAM J. Optim. 25(2), 856–881 (2015)

[16] Ghadimi, S., Lan, G., Zhang, H.: Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization. Math. Program. 155, 267–305 (2016)

[17] Dauphin, Y.N., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., Bengio, Y.: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In: Advances in Neural Information Processing Systems, pp. 2933–2941 (2014)

[18] Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.W.: Stochastic variational inference. J. Mach. Learn. Res. 14(1), 1303–1347 (2013)

[19] Lau, J.H., Newman, D., Baldwin, T.: Machine reading tea leaves: automatically evaluating topic coherence and topic model quality. In: EACL, pp. 530–539 (2014)