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Variational Bayesian neural networks combine the flexibility of deep learning with Bayesian uncertainty estimation.
A mean field theory learning algorithm for neural networks
Carsten Peterson · 1987
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A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
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Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
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Neural learning in structured parameter spaces-natural riemannian gradient
Shun-ichi Amari · 1997
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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New insights and perspectives on the natural gradient method
James Martens · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
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Distributed second-order optimization using kronecker-factored approximations
Jimmy Ba, Roger Grosse, and James Martens · 2016
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Multiplicative normalizing flows for variational Bayesian neural networks
Christos Louizos and Max Welling · 2017
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Learning structured weight uncertainty in bayesian neural networks
Shengyang Sun, Changyou Chen, and Lawrence Carin · 2017
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Noisy natural gradient as variational inference
Guodong Zhang, Shengyang Sun, David Duvenaud, and Roger Grosse · 2017
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Exact natural gradient in deep linear networks and application to the nonlinear case
Alberto Bernacchia, Máté Lengyel, and Guillaume Hennequin · 2018
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Fast approximate natural gradient descent in a kronecker-factored eigenbasis
Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, and Pascal Vincent · 2018
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Structured and efficient variational deep learning with matrix gaussian posteriors
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