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Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston. 2008 · 2008
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2014 · 2014
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. 2016 · 2016
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Reducing overfitting in deep networks by decorrelating representations
Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, and Dhruv Batra. 2016 · 2016
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Language as a latent variable: Discrete generative models for sentence compression
Yishu Miao and Phil Blunsom. 2016 · 2016
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Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom. 2016 · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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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
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
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A hybrid convolutional variational autoencoder for text generation
Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. 2017 · 2017
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Latent intention dialogue models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, and Steve Young. 2017 · 2017
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Variational autoencoder for semi-supervised text classification
Weidi Xu, Haoze Sun, Chao Deng, and Ying Tan. 2017 · 2017
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Improved variational autoencoders for text modeling using dilated convolutions
Semi-amortized variational autoencoders
Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, and Alexander Rush. 2018 · 2018
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A hierarchical latent structure for variational conversation modeling
Yookoon Park, Jaemin Cho, and Gunhee Kim. 2018 · 2018
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Spherical latent spaces for stable variational autoencoders
Jiacheng Xu and Greg Durrett. 2018 · 2018
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Avoiding latent variable collapse with generative skip models
Adji B Dieng, Yoon Kim, Alexander M Rush, and David M Blei. 2019 · 2019
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Implicit deep latent variable models for text generation
Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, and Changyou Chen. 2019 · 2019
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Lagging inference networks and posterior collapse in variational autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick. 2019 · 2019
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Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017 · 2017
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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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Improving gan training via binarized representation entropy (bre) regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui, and Ruitong Huang. 2018 · 2018
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Cyclical annealing schedule: A simple approach to mitigating kl vanishing
Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin, et al. 2019 · 2019
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Preventing posterior collapse with delta-vaes
Ali Razavi, Aäron van den Oord, Ben Poole, and Oriol Vinyals. 2019 · 2019
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Riemannian normalizing flow on variational wasserstein autoencoder for text modeling
Prince Zizhuang Wang and William Yang Wang. 2019 · 2019
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