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The Pachinko Allocation Machine (PAM) is a deep topic model that allows representing rich correlation structures among topics by a directed acyclic graph over topics.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
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Earlier work this paper cites.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
Earlier work this paper cites.
Hierarchical Topic Models and the Nested Chinese Restaurant Process
D.M. Blei, T.L. Griffiths, M.I. Jordan, and J.B. Tenenbaum. 2004 · 2003
Earlier work this paper cites.
Finding scientific topics
Thomas L Griffiths and Mark Steyvers. 2004 · 2004
Earlier work this paper cites.
Correlated topic models
David Blei and John Lafferty. 2006 · 2006
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Pachinko allocation: Dag-structured mixture models of topic correlations
Wei Li and Andrew McCallum. 2006 · 2006
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Mixtures of hierarchical topics with pachinko allocation
David Mimno, Wei Li, and Andrew McCallum. 2007 · 2007
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Reading tea leaves: How humans interpret topic models
Jonathan Chang, Jordan L Boyd-Graber, Sean Gerrish, Chong Wang, and David M Blei. 2009 · 2009
Earlier work this paper cites.
Replicated softmax: an undirected topic model
Geoffrey E Hinton and Ruslan R Salakhutdinov. 2009 · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
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Hugo Larochelle and Stanislas Lauly. 2012 · 2012
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Nonparametric bayes pachinko allocation
Wei Li, David Blei, and Andrew McCallum. 2012 · 2012
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Diederik P Kingma and Max Welling. 2013 · 2013
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M. Lichman. 2013 · 2013
Cited alongside, same era.
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015 · 2015
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Danilo Rezende and Shakir Mohamed. 2015 · 2015
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Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei. 2016 · 2016
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Diederik Kingma and Jimmy Ba. 2014 · 2014
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Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality
Jey Han Lau, David Newman, and Timothy Baldwin. 2014 · 2014
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Rajesh Ranganath, Sean Gerrish, and David M Blei. 2014 · 2014
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Francisco R Ruiz, Michalis Titsias RC AUEB, and David Blei. 2016 · 2016
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Mohammad Emtiyaz Khan and Wu Lin. 2017 · 2017
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Autoencoding variational inference for topic models
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