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Topic models have been widely explored as probabilistic generative models of documents.
Inference for nonconjugate bayesian models using the gibbs sampler
Carlin, Bradley P and Polson, Nicholas G · 1991
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A constructive definition of dirichlet priors
Sethuraman, Jayaram · 1994
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An introduction to latent semantic analysis
Landauer, Thomas K, Foltz, Peter W, and Laham, Darrell · 1998
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Probabilistic latent semantic indexing
Hofmann, Thomas · 1999
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An introduction to variational methods for graphical models
Jordan, Michael I, Ghahramani, Zoubin, Jaakkola, Tommi S, and Saul, Lawrence K · 1999
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A variational bayesian framework for graphical models
Attias, Hagai · 2000
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Variational algorithms for approximate Bayesian inference
Beal, Matthew James · 2003
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Latent dirichlet allocation
Blei, David M, Ng, Andrew Y, and Jordan, Michael I · 2003
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The author-topic model for authors and documents
Rosen-Zvi, Michal, Griffiths, Thomas, Steyvers, Mark, and Smyth, Padhraic · 2004
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Dynamic topic models
Blei, David M and Lafferty, John D · 2006
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A tutorial on energy-based learning
LeCun, Yann, Chopra, Sumit, and Hadsell, Raia · 2006
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Hierarchical dirichlet processes
Teh, Yee Whye, Jordan, Michael I, Beal, Matthew J, and Blei, David M · 2006
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Topics over time: a non-markov continuous-time model of topical trends
Wang, Xuerui and McCallum, Andrew · 2006
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A correlated topic model of science
Blei, David M and Lafferty, John D · 2007
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Supervised topic models
Mcauliffe, Jon D and Blei, David M · 2008
Cited alongside, same era.
Replicated softmax: an undirected topic model
Hinton, Geoffrey E and Salakhutdinov, Ruslan · 2009
Cited alongside, same era.
Online learning for latent dirichlet allocation
Hoffman, Matthew, Bach, Francis R, and Blei, David M · 2010
Cited alongside, same era.
The million song dataset
Bertin-Mahieux, Thierry, Ellis, Daniel P.W., Whitman, Brian, and Lamere, Paul · 2011
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2014
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Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality
Lau, Jey Han, Newman, David, and Baldwin, Timothy · 2014
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Neural variational inference and learning in belief networks
Mnih, Andriy and Gregor, Karol · 2014
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Recurrent models of visual attention
Mnih, Volodymyr, Heess, Nicolas, and Graves, Alex · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo J, Mohamed, Shakir, and Wierstra, Daan · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik P. and Ba, Jimmy · 2015
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Wang, Chong, Paisley, John William, and Blei, David M · 2011
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Truly nonparametric online variational inference for hierarchical dirichlet processes
Bryant, Michael and Sudderth, Erik B · 2012
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A stick-breaking likelihood for categorical data analysis with latent gaussian models
Khan, Mohammad Emtiyaz, Mohamed, Shakir, Marlin, Benjamin M, and Murphy, Kevin P · 2012
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A neural autoregressive topic model
Larochelle, Hugo and Lauly, Stanislas · 2012
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Variational inference in nonconjugate models
Wang, Chong and Blei, David M · 2013
Cited alongside, same era.
Diversifying restricted boltzmann machine for document modeling
Xie, Pengtao, Deng, Yuntian, and Xing, Eric · 2015
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Topicrnn: A recurrent neural network with long-range semantic dependency
Dieng, Adji B, Wang, Chong, Gao, Jianfeng, and Paisley, John · 2016
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Neural variational inference for text processing
Miao, Yishu, Yu, Lei, and Blunsom, Phil · 2016
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Deep generative models with stick-breaking priors
Nalisnick, Eric and Smyth, Padhraic · 2016
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Neural variational inference for topic models
Srivastava, Akash and Sutton, Charles · 2016
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