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A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typically multivariate Gaussians.
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Piecewise Latent Variables for Neural Variational Text Processing
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Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Variational Autoencoder for Semi-Supervised Text Classification
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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
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Pixel Recurrent Neural Networks
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WaveNet: A Generative Model for Raw Audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016b
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A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Iulian Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017b
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Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2017
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Fixing a Broken ELBO
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif Saurous, and Kevin Murphy. 2018 · 2018
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Hyperspherical Variational Auto-Encoders
Tim Davidson, Luca Falorsi, Nicola Cao, Thomas Kipf, and Jakub Tomczak. 2018 · 2018
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Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, and Percy Liang. 2018 · 2018
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