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Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to complex models such as deep neural networks.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Natural gradient works efficiently in learning
Shun-ichi Amari · 1998
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Expectation propagation for approximate Bayesian inference
T. Minka · 2001
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Unsupervised variational Bayesian learning of nonlinear models
Antti Honkela and Harri Valpola · 2004
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Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Graphical models, exponential families, and variational inference
M. J. Wainwright and M. I. Jordan · 2008
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Statistical exponential families: A digest with flash cards
Frank Nielsen and Vincent Garcia · 2009
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Practical variational inference for neural networks
Alex Graves · 2011
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Fast variational inference in the conjugate exponential family
James Hensman, Magnus Rattray, and Neil D Lawrence · 2012
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Diederik P Kingma and Max Welling · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Tim Salimans and David Knowles · 2013
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Bayesian data analysis
Andrew Gelman, John B Carlin, Hal S Stern, and Donald B Rubin · 2014
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David M Blei · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Information geometry and its applications
Shun-ichi Amari · 2016
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Conjugate-computation variational inference: converting variational inference in non-conjugate models to inferences in conjugate models
Mohammad Emtiyaz Khan and Wu Lin · 2017
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Fast and scalable Bayesian deep learning by weight-pertubation in Adam
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, and Akash Srivastava · 2018
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Natural gradients in practice: Non-conjugate variational inference in gaussian process models
Hugh Salimbeni, Stefanos Eleftheriadis, and James Hensman · 2018
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