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Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks.
Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson. 2019 · 1902
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Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993 · 1993
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Switchboard-1 release 2: Linguistic data consortium
J Godfrey and E Holliman. 1997 · 1997
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Long short-term memory
Sepp Hochreiter and Jurgen Schmidhuber. 1997 · 1997
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
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Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010 · 2010
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley. 2013 · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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A systematic comparison of smoothing techniques for sentence-level bleu
Boxing Chen and Colin Cherry. 2014 · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. 2015 · 2015
Earlier work this paper cites.
Sequential neural models with stochastic layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther. 2016 · 2016
Earlier work this paper cites.
Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson. 2016 · 2016
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Chia-Wei Liu, Ryan Lowe, Iulian V Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 2016 · 2016
Cited alongside, same era.
Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2016 · 2016
Cited alongside, same era.
Language as a latent variable: Discrete generative models for sentence compression
Yishu Miao and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016 · 2016
Cited alongside, same era.
Latent intention dialogue models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, and Steve Young. 2017 · 2017
Later among the works it cites.
Variational autoencoder for semi-supervised text classification
Weidi Xu, Haoze Sun, Chao Deng, and Ying Tan. 2017 · 2017
Later among the works it cites.
Improved variational autoencoders for text modeling using dilated convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017 · 2017
Later among the works it cites.
Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017 · 2017
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Fixing a broken ELBO
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A Saurous, and Kevin Murphy. 2018 · 2018
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Isolating sources of disentanglement in VAEs
Ricky TQ Chen, Xuechen Li, Roger Grosse, and David Duvenaud. 2018 · 2018
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Z-forcing: Training stochastic recurrent networks
Anirudh Goyal Alias Parth Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
Cited alongside, same era.
Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger. 2017 · 2017
Cited alongside, same era.
ALICE: Towards understanding adversarial learning for joint distribution matching
Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, and Lawrence Carin. 2017 · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Cited alongside, same era.
Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Avoiding latent variable collapse with generative skip models
Adji B Dieng, Yoon Kim, Alexander M Rush, and David M Blei. 2018 · 2018
Later among the works it cites.
Disentangling by factorising
Hyunjik Kim and Andriy Mnih. 2018 · 2018
Later among the works it cites.
Semi-amortized variational autoencoders
Yoon Kim, Sam Wiseman, Andrew C Miller, David Sontag, and Alexander M Rush. 2018 · 2018
Later among the works it cites.
Stochastic wavenet: A generative latent variable model for sequential data
Guokun Lai, Bohan Li, Guoqing Zheng, and Yiming Yang. 2018 · 2018
Later among the works it cites.
Amortized inference regularization
Rui Shu, Hung H Bui, Shengjia Zhao, Mykel J Kochenderfer, and Stefano Ermon. 2018 · 2018
Later among the works it cites.
Lagging inference networks and posterior collapse in variational autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick. 2019 · 2019
Closest in time.
InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2019 · 2019
Closest in time.