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Deep neural networks(NNs) have achieved impressive performance, often exceed human performance on many computer vision tasks.
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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Understanding the metropolis-hastings algorithm
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Ensemble learning in bayesian neural networks
David Barber and Christopher M Bishop · 1998
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Markov chain monte carlo posterior sampling with the hamiltonian method
Kenneth M Hanson · 2001
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On bayesian model and variable selection using mcmc
Petros Dellaportas, Jonathan J Forster, and Ioannis Ntzoufras · 2002
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Bridging the gap between stochastic gradient mcmc and stochastic optimization
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Preconditioned stochastic gradient langevin dynamics for deep neural networks
Chunyuan Li, Changyou Chen, David E Carlson, and Lawrence Carin · 2016
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