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Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models.
Reverend Bayes on inference engines: A distributed hierarchical approach
Pearl, Judea · 1982
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Sample-based non-uniform random variate generation
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Learning representations by back-propagating errors
Rumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J · 1986
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Hybrid Monte Carlo
Duane, Simon, Kennedy, Anthony D, Pendleton, Brian J, and Roweth, Duncan · 1987
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A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms
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Efficient parameterisations for normal linear mixed models
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Markov chain Monte Carlo in practice: A roundtable discussion
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Fast EM-type implementations for mixed effects models
Meng, X-L and Van Dyk, David · 1998
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Variational learning in nonlinear Gaussian belief networks
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Causality: models, reasoning and inference , volume 29
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Expectation propagation for approximate bayesian inference
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Slice sampling
Neal, Radford M · 2003
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Non-centered parameterisations for hierarchical models and data augmentation
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Nonlinear deterministic relationships in Bayesian networks
Cobb, Barry R and Shenoy, Prakash P · 2005
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A general framework for the parametrization of hierarchical models
Papaspiliopoulos, Omiros, Roberts, Gareth O, and Sköld, Martin · 2007
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoff · 2012
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Deep learning made easier by linear transformations in perceptrons
Raiko, Tapani, Valpola, Harri, and LeCun, Yann · 2012
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Estimating or propagating gradients through stochastic neurons
Bengio, Yoshua · 2013
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Deep generative stochastic networks trainable by backprop
Bengio, Yoshua and Thibodeau-Laufer, Éric · 2013
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Goodfellow, Ian J, Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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Learning with marginalized corrupted features
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