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There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.
Modeling high-dimensional discrete data with multi-layer neural networks
Bengio, Yoshua and Bengio, Samy · 2000
Earlier work this paper cites.
On the quantitative analysis of deep belief networks
Salakhutdinov, Ruslan and Murray, Iain · 2008
Earlier work this paper cites.
Generative versus discriminative training of RBMs for classification of fMRI images
Schmah, Tanya, Hinton, Geoffrey E., Zemel, Richard S., Small, Steven L., and Strother, Stephen C · 2009
Earlier work this paper cites.
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
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
Duchi, John, Hazan, Elad, and Singer, Yoram · 2010
Earlier work this paper cites.
Learning representations by maximizing compression, 2011
Gregor, Karol and LeCun, Yann · 2011
Earlier work this paper cites.
The neural autoregressive distribution estimator
Larochelle, Hugo and Murray, Iain · 2011
Earlier work this paper cites.
Sum-product networks: A new deep architecture
Poon, Hoifung and Domingos, Pedro · 2011
Cited alongside, same era.
Theano: new features and speed improvements
Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian J., Bergeron, Arnaud, Bouchard, Nicolas, and Bengio, Yoshua · 2012
Cited alongside, same era.
ADADELTA: an adaptive learning rate method, 2012
Zeiler, Matthew D · 2012
Cited alongside, same era.
RNADE: The real-valued neural autoregressive density-estimator
Uria, Benigno, Murray, Iain, and Larochelle, Hugo · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
Wan, Li, Zeiler, Matthew D., Zhang, Sixin, LeCun, Yann, and Fergus, Rob · 2013
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Bengio, Yoshua, Laufer, Eric, Alain, Guillaume, and Yosinski, Jason · 2014
Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Later among the works it cites.
Deep AutoRegressive Networks
Gregor, Karol, Danihelka, Ivo, Mnih, Andriy, Blundell, Charles, and Wierstra, Daan · 2014
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Auto-encoding variational bayes
Kingma, Diederik P. and Welling, Max · 2014
Later among the works it cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
Later among the works it cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
Later among the works it cites.
A deep and tractable density estimator
Uria, Benigno, Murray, Iain, and Larochelle, Hugo · 2014
Later among the works it cites.
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Cited alongside, same era.
NICE: non-linear independent components estimation, 2014
Dinh, Laurent, Krueger, David, and Bengio, Yoshua · 2014
Cited alongside, same era.
Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE
Uria, Benigno, Murray, Iain, Renals, Steve, and Valentini-Botinhao, Cassia · 2015
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