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This paper presents a novel communication-efficient parallel belief propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA).
Human behavior and the principle of least effort
G.K. Zipf · 1949
Earlier work this paper cites.
Latent dirichlet allocation
D.M. Blei, A.Y. Ng, and M.I. Jordan · 2003
Earlier work this paper cites.
Finding scientific topics
T. L. Griffiths and M. Steyvers · 2004
Earlier work this paper cites.
Power laws, pareto distributions and zipf’s law
M.E.J. Newman · 2005
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Distributed algorithms for topic models
D. Newman, A. Asuncion, P. Smyth, and M. Welling · 2008
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Distributed algorithms for topic models
D. Newman, A. Asuncion, P. Smyth, and M. Welling · 2009
Cited alongside, same era.
Online inference of topics with latent dirichlet allocation
K.R. Canini, L. Shi, and T.L. Griffiths · 2009
Cited alongside, same era.
Plda: Parallel latent dirichlet allocation for large-scale applications
Y. Wang, H. Bai, M. Stanton, W.Y. Chen, and E. Chang · 2009
Cited alongside, same era.
Online learning for latent dirichlet allocation
M.D. Hoffman, D.M. Blei, and F. Bach · 2010
Later among the works it cites.
Using variational inference and mapreduce to scale topic modeling
Ke Zhai, Jordan L. Boyd-Graber, and Nima Asadi · 2011
Later among the works it cites.
Learning topic models by belief propagation
Jia Zeng, William K. Cheung, and Jiming Liu · 2011
Later among the works it cites.
Plda+: Parallel latent dirichlet allocation with data placement and pipeline processing
Z. Liu, Y. Zhang, E.Y. Chang, and M. Sun · 2011
Later among the works it cites.
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