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Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems.
Learning representations by back-propagating errors
Rumelhart, D.E., Hintont, G.E., and Williams, R.J · 1986
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Multilayer feedforward networks are universal approximators
Hornik, Kurt, Stinchcombe, Maxwell, and White, Halbert · 1989
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Keeping neural networks simple by minimizing the description length of the weights
Hinton, Geoffrey and Camp, Drew Van · 1993
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Bayesian learning for neural networks
Neal, Radford M · 1995
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A comparison of sequential learning methods for incomplete data
Cowell, R. G., Dawid, P. A., and Sebastiani, P · 1996
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A Bayesian approach to on-line learning
Opper, Manfred and Winther, Ole · 1998
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A family of algorithms for approximate Bayesian inference
Minka, Thomas P · 2001
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MCMC methods for MLP-network and Gaussian process and stuff–a documentation for Matlab toolbox MCMCstuff
Vanhatalo, Jarno and Vehtari, Aki · 2006
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Soudry, Daniel, Hubara, Itay, and Meir, Ron · 2014
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MacKay, David J. C
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Bayesian methods for adaptive models
MacKay, David J. C
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MacKay, David J. C
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