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Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs).
Verification of Forecasts Expressed in Terms of Probability
Glenn W Brier · 1950
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An Empirical Bayes Approach to Statistics
Herbert E Robbins · 1956
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Sequential Updating of Conditional Probabilities on Directed Graphical Structures
David J Spiegelhalter and Steffen L Lauritzen · 1990
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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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Bayesian Learning via Stochastic Dynamics
Radford M Neal · 1993
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Bayesian Learning for Neural Networks
Radford M Neal · 1995
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Bayesian Gaussian Processes for Regression and Classification
Mark N Gibbs · 1997
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Christopher M. Bishop · 2006
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80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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