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Variational inference lies at the core of many state-of-the-art algorithms.
“Hybrid Monte Carlo”
Simon Duane, A.D. Kennedy, Brian˜J. Pendleton and Duncan Roweth · 1987
Earlier work this paper cites.
“A generalized guided Monte Carlo algorithm”
Alan˜M. Horowitz · 1991
Earlier work this paper cites.
“Gradient Based Learning Applied to Document Recognition”
Yann LeCun, Leon Bottou, Yoshua Bengio and Patrick Haffner · 1998
Earlier work this paper cites.
“Introduction to variational methods for graphical models”
Michael˜I. Jordan, Zoubin Ghahramani, Tommi˜S. Jaakkola and Lawrence˜K. Saul · 1999
Earlier work this paper cites.
“General state space Markov chains and MCMC algorithms.”
Gareth˜O Roberts and Jeffrey˜S Rosenthal · 2004
Earlier work this paper cites.
“On the quantitative analysis of Deep Belief Networks”
Ruslan Salakhutdinov and Iain Murray · 2008
Earlier work this paper cites.
“Theano: a CPU and GPU math compiler in Python”
James Bergstra, Olivier Breuleux, Frederic Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley and Yoshua Bengio · 2010
Earlier work this paper cites.
“Riemann manifold Langevin and Hamiltonian Monte Carlo methods”
Mark Girolami and Ben Calderhead · 2010
Cited alongside, same era.
“MCMC using Hamiltonian dynamics”
Radford˜M. Neal · 2011
Cited alongside, same era.
“Theano: new features and speed improvements”
Fr“’ed“’eric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley and Yoshua Bengio · 2012
Cited alongside, same era.
“Improving neural networks by preventing co-adaptation of feature detectors”, 2012
Geoffrey˜E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever and Ruslan˜R. Salakhutdinov · 2012
Cited alongside, same era.
“Stochastic Variational Inference”
Matt Hoffman, David˜M. Blei, Chong Wang and John Paisley · 2013
Cited alongside, same era.
“Stochastic Backpropagation and Approximate Inference in Deep Generative Models”
Danilo˜Jimenez Rezende, Shakir Mohamed and Daan Wierstra · 2014
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“climin - A pythonic framework for gradient-based function optimization”, 2015
Justin Bayer, Christian Osendorfer, Sarah Diot-Girard, Thomas R“”uckstiess and Sebastian Urban · 2015
Later among the works it cites.
“DRAW: A Recurrent Neural Network For Image Generation”
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende and Daan Wierstra · 2015
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“Adam: a Method for Stochastic Optimization”
Diederik˜P. Kingma and Jimmy˜Lei Ba · 2015
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“Variational Inference with Normalizing Flows”
Danilo˜Jimenez Rezende and Shakir Mohamed · 2015
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M˜J Betancourt, Simon Byrne, Samuel Livingstone and Mark Girolami · 2014
Cited alongside, same era.
“Stochastic Gradient VB and the Variational Auto-Encoder”
Diederik˜P Kingma and Max Welling · 2014
Cited alongside, same era.
Tim Salimans, Diederik˜P. Kingma and Max Welling · 2015
Later among the works it cites.