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A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions.
Neural embedding allocation: Distributed representations of topic models
Kamrun Naher Keya, Yannis Papanikolaou, and James R. Foulds · 1909
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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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Rcv1: A new benchmark collection for text categorization research
D. D. Lewis, Y. Yang, T. G. Rose, and F. Li · 2004
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Hierarchical Dirichlet processes
Yee Whye Teh, Michael I Jordan, Matthew J Beal, and David M Blei · 2006
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Learning to classify short and sparse text & web with hidden topics from large-scale data collections
Xuan-Hieu Phan, Le-Minh Nguyen, and Susumu Horiguchi · 2008
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Introduction to information retrieval , volume 39
Hinrich Schütze, Christopher D Manning, and Prabhakar Raghavan · 2008
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Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Online learning for latent Dirichlet allocation
Matthew Hoffman, Francis R Bach, and David M Blei · 2010
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Word features for latent Dirichlet allocation
James Petterson, Alexander J Smola, Tibério S Caetano, Wray L Buntine, Shravan M Narayanamurthy, et al · 2010
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Classification of short texts by deploying topical annotations
Daniele Vitale, Paolo Ferragina, and Ugo Scaiella · 2012
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Beta-negative binomial process and Poisson factor analysis
Mingyuan Zhou, Lauren Hannah, David Dunson, and Lawrence Carin · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 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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Learning with a Wasserstein loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, and Tomaso A. Poggio · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al · 2015
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Improving topic models with latent feature word representations
Dat Quoc Nguyen, Richard Billingsley, Lan Du, and Mark Johnson · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Cited alongside, same era.
Topic modeling for short texts with auxiliary word embeddings
Distilled Wasserstein learning for word embedding and topic modeling
Hongteng Xu, Wenlin Wang, Wei Liu, and Lawrence Carin · 2018
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WHAI: Weibull hybrid autoencoding inference for deep topic modeling
Hao Zhang, Bo Chen, Dandan Guo, and Mingyuan Zhou · 2018
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Decoupling sparsity and smoothness in the Dirichlet variational autoencoder topic model
Sophie Burkhardt and Stefan Kramer · 2019
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Topic modeling with Wasserstein autoencoders
Feng Nan, Ran Ding, Ramesh Nallapati, and Bing Xiang · 2019
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Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
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Hierarchical optimal transport for document representation
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, and Justin M. Solomon · 2019
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Chenliang Li, Haoran Wang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma · 2016
Cited alongside, same era.
Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom · 2016
Cited alongside, same era.
Mixing Dirichlet topic models and word embeddings to make lda2vec
Christopher E. Moody · 2016
Cited alongside, same era.
Augmentable gamma belief networks
Mingyuan Zhou, Yulai Cong, and Bo Chen · 2016
Cited alongside, same era.
Topic modeling of short texts: A pseudo-document view
Yuan Zuo, Junjie Wu, Hui Zhang, Hao Lin, Fei Wang, Ke Xu, and Hui Xiong · 2016
Cited alongside, same era.
Jointly learning word embeddings and latent topics
Bei Shi, Wai Lam, Shoaib Jameel, Steven Schockaert, and Kwun Ping Lai · 2017
Cited alongside, same era.
Autoencoding variational inference for topic models
Akash Srivastava and Charles Sutton · 2017
Cited alongside, same era.
Later among the works it cites.
Topic modeling in embedding spaces
Adji B Dieng, Francisco JR Ruiz, and David M Blei · 2020
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Recurrent hierarchical topic-guided RNN for language generation
Dandan Guo, Bo Chen, Ruiying Lu, and Mingyuan Zhou · 2020
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OTLDA: A geometry-aware optimal transport approach for topic modeling
Viet Huynh, He Zhao, and Dinh Phung · 2020
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Decoupled word embeddings using latent topics
Heesoo Park and Jongwuk Lee · 2020
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Deep relational topic modeling via graph Poisson gamma belief network
Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, and Mingyuan Zhou · 2020
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Neural topic model via optimal transport, 2020
He Zhao, D Phung, V Huynh, T Le, and W Buntine · 2020
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Comparing probability distributions with conditional transport
Huangjie Zheng and Mingyuan Zhou · 2020
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A prototype-oriented framework for unsupervised domain adaptation
Korawat Tanwisuth, XINJIE FAN, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, and Mingyuan Zhou · 2021
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Topic modelling meets deep neural networks: A survey
He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, and Wray Buntine · 2021
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