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Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation.
A useful theorem for nonlinear devices having gaussian inputs
Price, Robert · 1958
Earlier work this paper cites.
Transformations des signaux aléatoires a travers les systemes non linéaires sans mémoire
Bonnet, Georges · 1964
Earlier work this paper cites.
A mean field theory learning algorithm for neural networks
Peterson, Carsten · 1987
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, Ronald J · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Hinton, Geoffrey E and Van Camp, Drew · 1993
Earlier work this paper cites.
BAYESIAN LEARNING FOR NEURAL NETWORKS
Neal, Radford M · 1995
Earlier work this paper cites.
Neural learning in structured parameter spaces-natural riemannian gradient
Amari, Shun-ichi · 1997
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Natural gradient works efficiently in learning
Amari, Shun-Ichi · 1998
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A view of the em algorithm that justifies incremental, sparse, and other variants
Neal, Radford M and Hinton, Geoffrey E · 1998
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, Alexandru and Caruana, Rich · 2005
Earlier work this paper cites.
Uci machine learning repository, 2007
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Learning multiple layers of features from tiny images
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The variational gaussian approximation revisited
Opper, Manfred and Archambeau, Cédric · 2009
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Active learning literature survey
Settles, Burr · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
Duchi, John, Hazan, Elad, and Singer, Yoram · 2011
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Practical variational inference for neural networks
Graves, Alex · 2011
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Mcmc using hamiltonian dynamics
Neal, Radford M et al · 2011
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Staines, Joe and Barber, David · 2012
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Stochastic variational inference
Hoffman, Matthew D, Blei, David M, Wang, Chong, and Paisley, John · 2013
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
Variational dropout and the local reparameterization trick
Kingma, Diederik P, Salimans, Tim, and Welling, Max · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
Martens, James and Grosse, Roger · 2015
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Variational inference with normalizing flows
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Trust region policy optimization
Schulman, John, Levine, Sergey, Abbeel, Pieter, Jordan, Michael, and Moritz, Philipp · 2015
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Distributed second-order optimization using kronecker-factored approximations
Ba, Jimmy, Grosse, Roger, and Martens, James · 2016
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A kronecker-factored approximate fisher matrix for convolution layers
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Structured and efficient variational deep learning with matrix gaussian posteriors
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Distributed second-order optimization using kronecker-factored approximations
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On calibration of modern neural networks
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Multiplicative normalizing flows for variational bayesian neural networks
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