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The role of uncertainty quantification (UQ) in deep learning has become crucial with growing use of predictive models in high-risk applications.
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David A Nix and Andreas S Weigend, · 1994
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John Platt et al., · 1999
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Roger Koenker and Kevin F Hallock, · 2001
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Alexandru Niculescu-Mizil and Rich Caruana, · 2005
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“Probabilistic forecasts, calibration and sharpness,”
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery, · 2007
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Uncertainty quantification: theory, implementation, and applications
Ralph C Smith, · 2013
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“Probabilistic machine learning and artificial intelligence,”
Zoubin Ghahramani, · 2015
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“Dropout as a bayesian approximation: Representing model uncertainty in deep learning,”
Yarin Gal and Zoubin Ghahramani, · 2016
Cited alongside, same era.
Uncertainty in deep learning
Yarin Gal, · 2016
Cited alongside, same era.
“Towards a rigorous science of interpretable machine learning,”
Finale Doshi-Velez and Been Kim, · 2017
Cited alongside, same era.
“Simple and scalable predictive uncertainty estimation using deep ensembles,”
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell, · 2017
Cited alongside, same era.
“Concrete dropout,”
Yarin Gal, Jiri Hron, and Alex Kendall, · 2017
Cited alongside, same era.
“What uncertainties do we need in bayesian deep learning for computer vision?,”
Alex Kendall and Yarin Gal, · 2017
Laurence Perreault Levasseur, Yashar D Hezaveh, and Risa H Wechsler, · 2017
Later among the works it cites.
“Methods for interpreting and understanding deep neural networks,”
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller, · 2018
Later among the works it cites.
“Frequentist uncertainty estimates for deep learning,”
Natasa Tagasovska and David Lopez-Paz, · 2018
Later among the works it cites.
“Accurate uncertainties for deep learning using calibrated regression,”
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon, · 2018
Later among the works it cites.
“Understanding deep neural networks through input uncertainties,”
Jayaraman J Thiagarajan, Irene Kim, Rushil Anirudh, and Peer-Timo Bremer, · 2019
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Cited alongside, same era.
“On calibration of modern neural networks,”
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger, · 2017
Cited alongside, same era.
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“Building calibrated deep models via uncertainty matching with auxiliary interval predictors,”
Jayaraman J Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, and Peer-Timo Bremer, · 2019
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“Evaluating and calibrating uncertainty prediction in regression tasks,”
Dan Levi, Liran Gispan, Niv Giladi, and Ethan Fetaya, · 2019
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