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Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders.
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Modeling documents with deep boltzmann machines
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Auto-encoding variational Bayes
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Neural variational inference and learning in belief networks
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Stochastic backpropagation and approximate inference in deep generative models
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A deep and tractable density estimator
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Discovering latent structure in task-oriented dialogues
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Reweighted wake-sleep
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Sequential neural models with stochastic layers
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Composing graphical models with neural networks for structured representations and fast inference
M. Johnson, D. K. Duvenaud, A. Wiltschko, R. P. Adams, and S. R. Datta. 2016 · 2016
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Smart Reply: Automated Response Suggestion for Email
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Improving variational inference with inverse autoregressive flow
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Autoencoding beyond pixels using a learned similarity metric
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DRAW: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, and D. Wierstra. 2015 · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba. 2015 · 2015
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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
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For Sympathetic Ear, More Chinese Turn to Smartphone Program
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Online semi-supervised learning with deep hybrid boltzmann machines and denoising autoencoders
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S. Lauly, Y. Zheng, A. Allauzen, and H. Larochelle. 2016 · 2016
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Auxiliary deep generative models
L. Maaløe, C. K. Sønderby, S. K. Sønderby, and O. Winther. 2016 · 2016
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Neural variational inference for text processing
Y. Miao, L. Yu, and P. Blunsom. 2016 · 2016
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Hierarchical variational models
R. Ranganath, D. Tran, and D. Blei. 2016 · 2016
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The generalized reparameterization gradient
F. J. R. Ruiz, M. K. Titsias, and D. M. Blei. 2016 · 2016
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Neural machine translation of rare words with subword units
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Variational lossy autoencoder
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Categorical reparameterization with gumbel-softmax
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Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus
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The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh. 2017 · 2017
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Discrete variational autoencoders
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Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
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