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Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm.
Multiple hypergeometric functions: Probabilistic interpretations and statistical uses
James M Dickey · 1983
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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The helmholtz machine
Peter Dayan, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel · 1995
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Choice of basis for Laplace approximation
David JC MacKay · 1998
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Probabilistic latent semantic indexing
Thomas Hofmann · 1999
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A generalization of principal component analysis to the exponential family
Michael Collins, Sanjoy Dasgupta, and Robert E Schapire · 2001
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Mallet: A machine learning for language toolkit
Andrew McCallum · 2002
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Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Finding scientific topics
Thomas L Griffiths and Mark Steyvers · 2004
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Exponential family harmoniums with an application to information retrieval
Max Welling, Michal Rosen-Zvi, and Geoffrey E Hinton · 2004
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A Bayesian hierarchical model for learning natural scene categories
Li Fei-Fei and Pietro Perona · 2005
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The latent process decomposition of cdna microarray data sets
Simon Rogers, Mark Girolami, Colin Campbell, and Rainer Breitling · 2005
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Correlated topic models
David M. Blei and John D. Lafferty · 2006
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A correlated topic model of science
David M. Blei and John D. Lafferty · 2007
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Replicated softmax: an undirected topic model
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2009
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Reconstructing Pompeian households
David Mimno · 2009
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Rethinking LDA: Why priors matter
A neural autoregressive topic model
Hugo Larochelle and Stanislas Lauly · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 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
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David M Blei · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Hanna Wallach, David Mimno, and Andrew McCallum · 2009
Cited alongside, same era.
Online learning for latent dirichlet allocation
Matthew Hoffman, Francis R Bach, and David M Blei · 2010
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Automatic evaluation of topic coherence
David Newman, Jey Han Lau, Karl Grieser, and Timothy Baldwin · 2010
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Probabilistic topic models
David Blei · 2012
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Kernel topic models
Philipp Hennig, David H Stern, Ralf Herbrich, and Thore Graepel · 2012
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Training neural Bayesian nets
Laurent Dinh and Vincent Dumoulin · 2016
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Automatic differentiation variational inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei · 2016
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Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom · 2016
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