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Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over these features.
A mean field theory learning algorithm for neural networks
James R Anderson and Carsten Peterson · 1987
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Bayesian back-propagation
Wray L Buntine and Andreas S Weigend · 1991
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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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Gaussian Processes for Machine Learning
CE. Rasmussen and CKI. Williams · 2006
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Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
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Towards an empirical foundation for assessing bayesian optimization of hyperparameters
Katharina Eggensperger, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger H. Hoos, and Kevin Leyton-Brown · 2013
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, and Ryan P. Adams · 2015
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Manifold gaussian processes for regression
Roberto Calandra, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Bayesian optimization with robust bayesian neural networks
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, and Frank Hutter · 2016
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Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
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An empirical study on the properties of random bases for kernel methods
Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Fei Sha · 2017
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Variational inference for gaussian process models with linear complexity
Ching-An Cheng and Byron Boots · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Learning qualitatively diverse and interpretable rules for classification
Andrew Slavin Ross, Weiwei Pan, and Finale Doshi-Velez · 2018
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A stochastic variational framework for recurrent gaussian processes models
César Lincoln C. Mattos and Guilherme A. Barreto · 2019
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Benchmarking the neural linear model for regression
Sebastian W. Ober and Carl Edward Rasmussen · 2019
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Bayesian batch active learning as sparse subset approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, and José Miguel Hernández-Lobato · 2019
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Functional variational bayesian neural networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, and Roger Grosse · 2019
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Graph convolutional Gaussian processes
Ian Walker and Ben Glocker · 2019
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Ensemble sampling
Xiuyuan Lu and Benjamin Van Roy · 2017
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Convolutional Gaussian Processes
Mark van der Wilk, Carl Edward Rasmussen, and James Hensman · 2017
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
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Tim Pearce, Nicolas Anastassacos, Mohamed Zaki, and Andy Neely · 2018
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Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
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Pathologies of factorised gaussian and mc dropout posteriors in bayesian neural networks
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, and Richard E. Turner
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Exact gaussian processes on a million data points
Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q Weinberger, and Andrew Gordon Wilson · 2019
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Quality of uncertainty quantification for bayesian neural network inference
J. Yao, W. Pan, S. Ghosh, and F. Doshi-Velez · 2019
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Adaptive bayesian linear regression for automated machine learning
Weilin Zhou and Frederic Precioso · 2019
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 2020
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Uncertainty in neural networks: Approximately bayesian ensembling, 2020
Tim Pearce, Felix Leibfried, Alexandra Brintrup, Mohamed Zaki, and Andy Neely · 2020
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