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Conditional belief networks introduce stochastic binary variables in neural networks.
Recursive bayesian estimation using gaussian sums
Sorenson, H. W. and Alspach, D. L · 1971
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Connectionist learning of belief networks
Neal, Radford M · 1992
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Mixture density networks
Bishop, Christopher M · 1994
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies
Hochreiter, Sepp, Bengio, Yoshua, Frasconi, Paolo, and Schmidhuber, Jürgen · 2001
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Image denoising using scale mixtures of gaussians in the wavelet domain
Portilla, Javier, Strela, Vasily, Wainwright, Martin J, and Simoncelli, Eero P · 2003
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Fields of experts: A framework for learning image priors
Roth, Stefan and Black, Michael J · 2005
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Image denoising via sparse and redundant representations over learned dictionaries
Elad, Michael and Aharon, Michal · 2006
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A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Mallat, Stephane · 2008
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Bm3d image denoising with shape-adaptive principal component analysis
Dabov, Kostadin, Foi, Alessandro, Katkovnik, Vladimir, and Egiazarian, Karen · 2009
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A theoretical analysis of feature pooling in visual recognition
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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Online learning for matrix factorization and sparse coding
Mairal, Julien, Bach, Francis, Ponce, Jean, and Sapiro, Guillermo · 2010
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The toronto face database
Susskind, Joshua, Anderson, Adam, and Hinton, Geoffrey · 2010
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A spike and slab restricted boltzmann machine
Courville, Aaron C, Bergstra, James, and Bengio, Yoshua · 2011
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Goodfellow, Ian J, Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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Learning stochastic feedforward neural networks
Tang, Yichuan and Salakhutdinov, Ruslan R · 2013
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik P. and Ba, Jimmy · 2014
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Techniques for learning binary stochastic feedforward neural networks
Raiko, Tapani, Berglund, Mathias, Alain, Guillaume, and Dinh, Laurent · 2014
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Burger, Harold C, Schuler, Christian J, and Harmeling, Stefan · 2012
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Bengio, Yoshua, Léonard, Nicholas, and Courville, Aaron
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Deep generative stochastic networks trainable by backprop
Bengio, Yoshua, Thibodeau-Laufer, Eric, Alain, Guillaume, and Yosinski, Jason
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Generalized denoising auto-encoders as generative models
Bengio, Yoshua, Yao, Li, Alain, Guillaume, and Vincent, Pascal
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Learning to linearize under uncertainty
Goroshin, Ross, Mathieu, Michaël, and LeCun, Yann · 2015
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