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Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems.
A stochastic approximation method
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Online Learning for Latent Dirichlet Allocation. In NIPS
Matthew D. Hoffman, David M. Blei, and Francis R. Bach. 2010 · 2010
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Automatic Evaluation of Topic Coherence. In HLT-NAACL
David Newman, Jey Han Lau, Karl Grieser, and Timothy Baldwin. 2010 · 2010
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Alexander J. Smola and Shravan M. Narayanamurthy. 2010 · 2010
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A Comparison of Optimization Methods and Software for Large-scale L 1 {}^{\mbox{1}} -regularized Linear Classification
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Lecture Introduction to Computational Advertising
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Streaming Variational Bayes. In NIPS
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Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. In KDD
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Jian-Feng Yan, Jia Zeng, Zhi-Qiang Liu, and Yang Gao. 2013 · 2013
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Learning Topic Models by Belief Propagation
Jia Zeng, William K. Cheung, and Jiming Liu. 2013 · 2013
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Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, and Chih-Jen Lin. 2013 · 2013
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Annealing Paths for the Evaluation of Topic Models. In UAI
J. R. Foulds and P. Smyth. 2014 · 2014
Closest in time.
Distributed Stochastic Gradient MCMC. In ICML
B. Shahbaba S. Ahn and M. Welling. 2014 · 2014
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Communication-efficient algorithms for parallel latent Dirichlet allocation
Jian-Feng Yan, Jia Zeng, Yang Gao, and Zhi-Qiang Liu. 2014 · 2014
Closest in time.