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Recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods.
Connectionist learning of belief networks
Neal, Radford M · 1992
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The "wake-sleep" algorithm for unsupervised neural networks
Hinton, Geoffrey E, Dayan, Peter, Frey, Brendan J, and Neal, Radford M · 1995
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An introduction to variational methods for graphical models
Jordan, Michael I., Ghahramani, Zoubin, Jaakkola, Tommi S., and Saul, Lawrence K · 1999
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On the quantitative analysis of Deep Belief Networks
Salakhutdinov, Ruslan and Murray, Iain · 2008
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Learning stochastic feedforward neural networks
Tang, Yichuan and Salakhutdinov, Ruslan R · 2013
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2014
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Neural variational inference and learning in belief networks
Mnih, Andriy and Gregor, Karol · 2014
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Recurrent models of visual attention
Mnih, Volodymyr, Heess, Nicolas, and Graves, Alex · 2014
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Black box variational inference
Ranganath, Rajesh, Gerrish, Sean, and Blei, David M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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Learning wake-sleep recurrent attention models
Ba, Jimmy, Salakhutdinov, Ruslan R, Grosse, Roger B, and Frey, Brendan J · 2015
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Reweighted wake-sleep
Bornschein, Jörg and Bengio, Yoshua · 2015
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DRAW: A recurrent neural network for image generation
Gregor, Karol, Danihelka, Ivo, Graves, Alex, Rezende, Danilo Jimenez, and Wierstra, Daan · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
Cited alongside, same era.
Markov chain monte carlo and variational inference: Bridging the gap
Salimans, Tim, Kingma, Diederik P., and Welling, Max · 2015
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Learning structured output representation using deep conditional generative models
Sohn, Kihyuk, Lee, Honglak, and Yan, Xinchen · 2015
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Local expectation gradients for black box variational inference
Titsias, Michalis and Lázaro-Gredilla, Miguel · 2015
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Reinforcement learning neural Turing machines
Zaremba, Wojciech and Sutskever, Ilya · 2015
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Importance weighted autoencoders
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan · 2016
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Predicting distributions with linearizing belief networks
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Techniques for learning binary stochastic feedforward neural networks
Raiko, Tapani, Berglund, Mathias, Alain, Guillaume, and Dinh, Laurent · 2015
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Variational inference with normalizing flows
Rezende, Danilo Jimenez and Mohamed, Shakir · 2015
Cited alongside, same era.
Dauphin, Yann N and Grangier, David · 2016
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MuProp: Unbiased backpropagation for stochastic neural networks
Gu, Shixiang, Levine, Sergey, Sutskever, Ilya, and Mnih, Andriy · 2016
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