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In Bayesian Deep Learning, distributions over the output of classification neural networks are often approximated by first constructing a Gaussian distribution over the weights, then sampling from it to receive a distribution over the softmax outputs.
Sequential updating of conditional probabilities on directed graphical structures
David J Spiegelhalter and Steffen L Lauritzen · 1990
Earlier work this paper cites.
Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks
David J C Mackay · 1995
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Bayesian Gaussian Processes for Regression and Classification
Mark N. Gibbs · 1997
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The MNIST database of handwritten digits
Y. LeCun · 1998
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Choice of basis for laplace approximation
David J.C. MacKay · 1998
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Uncertainty estimation with infinitesimal jackknife, its distribution and mean-field approximation
Zhiyun Lu, Eugene Ie, and Fei Sha · 2006
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On tight approximate inference of the logistic-Normal topic admixture model
Amr Ahmed and Eric Xing · 2007
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Variational inference for large-scale models of discrete choice
Michael Braun and Jon McAuliffe · 2010
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Approximate Inference in Graphical Models
P. Hennig · 2010
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notMNIST dataset, 2011
Yaroslav Bulatov · 2011
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Practical Variational Inference for neural networks
Alex Graves · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Kernel topic models
P. Hennig, D. Stern, R. Herbrich, and T. Graepel · 2012
Earlier work this paper cites.
The CIFAR-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Optimizing neural networks with Kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Scalable Bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
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Evaluating uncertainty quantification in end-to-end autonomous driving control
Rhiannon Michelmore, Marta Kwiatkowska, and Yarin Gal · 2018
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A scalable laplace approximation for neural networks
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian neural networks
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, and Alexander L. Gaunt · 2018
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The need for uncertainty quantification in machine-assisted medical decision making
E. Begoli, T. Bhattacharya, and D. Kusnezov · 2019
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