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We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e.g., for applications involving bandits or active learning.
Compact approximations to bayesian predictive distributions
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Some comparisons among quadratic, spherical, and logarithmic scoring rules
J Eric Bickel · 2007
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Max Welling and Yee W Teh · 2011
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D. Rezende, S. Mohamed, and D. Wierstra · 2014
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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