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Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary random variables.
Over-relaxation method for the monte carlo evaluation of the partition function for multiquadratic actions
Adler, Stephen L · 1981
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Autoencoders, minimum description length, and helmholtz free energy
Hinton, Geoffrey E and Zemel, Richard S · 1994
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Bayesian Computation with R
Albert, Jim · 2009
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Mcmc using hamiltonian dynamics
Neal, Radford · 2011
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Theano: new features and speed improvements
Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian, Bergeron, Arnaud, Bouchard, Nicolas, Warde-Farley, David, and Bengio, Yoshua · 2012
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Variational bayesian inference with stochastic search
Paisley, John, Blei, David, and Jordan, Michael · 2012
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Fixed-form variational posterior approximation through stochastic linear regression
Salimans, Tim and Knowles, David A · 2013
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Learning to generate chairs with convolutional neural networks
Dosovitskiy, Alexey, Springenberg, Jost Tobias, and Brox, Thomas · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
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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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Black box variational inference
Ranganath, Rajesh, Gerrish, Sean, and Blei, David · 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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A deep and tractable density estimator
Uria, Benigno, Murray, Iain, and Larochelle, Hugo · 2014
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Draw: A recurrent neural network for image generation
Gregor, Karol, Danihelka, Ivo, Graves, Alex, and Wierstra, Daan · 2015
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