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Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes to existing code-bases.
Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Nonlinear programming
Dimitri P Bertsekas · 1999
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The Variational Gaussian Approximation Revisited
M. Opper and C. Archambeau · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Mohammad Emtiyaz Khan and Wu Lin · 2017
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Variational Adaptive-Newton Method for Explorative Learning
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, and Didrik Nielsen · 2017
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Stochastic gradient descent as approximate bayesian inference
Stephan Mandt, Matthew D Hoffman, and David M Blei · 2017
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Online natural gradient as a kalman filter, 2017
Yann Ollivier · 2017
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