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Efficient unsupervised training and inference in deep generative models remains a challenging problem.
The Helmholtz machine
Dayan, Peter, Hinton, Geoffrey E, Neal, Radford M, and Zemel, Richard S · 1995
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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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Varieties of helmholtz machine
Dayan, Peter and Hinton, Geoffrey E · 1996
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A fast learning algorithm for deep belief nets
Hinton, Geoffrey E., Osindero, Simon, and Teh, Yee Whye · 2006
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Learning deep architectures for AI
Bengio, Yoshua · 2009
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Evaluating probabilities under high-dimensional latent variable models
Murray, Iain and Salakhutdinov, Ruslan · 2009
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Introducing Monte Carlo Methods with R
Robert, Christian and Casella, George · 2009
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Deep boltzmann machines
Salakhutdinov, Ruslan and Hinton, Geoffrey E · 2009
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Theano: a CPU and GPU math expression compiler
Bergstra, James, Breuleux, Olivier, Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Desjardins, Guillaume, Turian, Joseph, Warde-Farley, David, and Bengio, Yoshua · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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The Toronto face dataset
Susskind, Joshua, Anderson, Adam, and Hinton, Geoffrey E · 2010
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Binarized mnist dataset, 2011
Larochelle, Hugo · 2011
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The Neural Autoregressive Distribution Estimator
Larochelle, Hugo and Murray, Ian · 2011
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
Neural variational inference and learning in belief networks
Mnih, Andriy and Gregor, Karol · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo J., Mohamed, Shakir, and Wierstra, Daan · 2014
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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Salimans, T., Kingma, D. P., and Welling, M · 2014
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Reweighted wake-sleep
Bornschein, Jorg and Bengio, Yoshua · 2015
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Importance weighted autoencoders
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan · 2015
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Iterative refinement of approximate posterior for training directed belief networks
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Auto-encoding variational bayes
Kingma, Durk P. and Welling, Max · 2014
Cited alongside, same era.
Hjelm, R Devon, Cho, Kyunghyun, Chung, Junyoung, Salakhutdinov, Russ, Calhoun, Vince, and Jojic, Nebojsa · 2015
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Blocks and fuel: Frameworks for deep learning
Van Merriënboer, Bart, Bahdanau, Dzmitry, Dumoulin, Vincent, Serdyuk, Dmitriy, Warde-Farley, David, Chorowski, Jan, and Bengio, Yoshua · 2015
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