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There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear.
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
Lev M Bregman · 1967
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Learning multiple layers of features from tiny images
A Krizhevsky · 2009
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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A generalized bias-variance decomposition for bregman divergences
David Pfau · 2013
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Tiny imagenet visual recognition challenge
Yann Le and Xuan Yang · 2015
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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Deep learning for finance: deep portfolios
James B Heaton, Nick G Polson, and Jan Hendrik Witte · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 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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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
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Deep learning in medical image analysis
Dinggang Shen, Guorong Wu, and Heung-Il Suk · 2017
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The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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Dropout-based active learning for regression
Evgenii Tsymbalov, Maxim Panov, and Alexander Shapeev · 2018
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Accurate uncertainty estimation and decomposition in ensemble learning
Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, and Brent Coull · 2019
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
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A survey of deep learning techniques for autonomous driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, and Gigel Macesanu · 2020
Understanding the bias-variance tradeoff of bregman divergences
Ben Adlam, Neha Gupta, Zelda Mariet, and Jamie Smith · 2022
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On uncertainty, tempering, and data augmentation in bayesian classification
Sanyam Kapoor, Wesley J Maddox, Pavel Izmailov, and Andrew G Wilson · 2022
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Uncertainty estimation of transformer predictions for misclassification detection
Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, et al · 2022
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On second-order scoring rules for epistemic uncertainty quantification
Viktor Bengs, Eyke Hüllermeier, and Willem Waegeman · 2023
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Lucas Berry and David Meger · 2023
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Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
Sajjad Mozaffari, Omar Y Al-Jarrah, Mehrdad Dianati, Paul Jennings, and Alexandros Mouzakitis · 2020
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Deep learning for financial applications: A survey
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Dropout strikes back: Improved uncertainty estimation via diversity sampling
Kirill Fedyanin, Evgenii Tsymbalov, and Maxim Panov · 2021
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2021
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Imprecise bayesian neural networks
Michele Caprio, Souradeep Dutta, Kuk Jin Jang, Vivian Lin, Radoslav Ivanov, Oleg Sokolsky, and Insup Lee · 2023
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Uncertainty estimates of predictions via a general bias-variance decomposition
Sebastian Gruber and Florian Buettner · 2023
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Deup: Direct epistemic uncertainty prediction
Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor I Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, and Yoshua Bengio · 2023
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Deep deterministic uncertainty: A new simple baseline
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip HS Torr, and Yarin Gal · 2023
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Introducing an improved information-theoretic measure of predictive uncertainty
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, and Sepp Hochreiter · 2023
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Efficient out-of-domain detection for sequence to sequence models
Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, and Artem Shelmanov · 2023
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Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, and Eyke Hüllermeier · 2023
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A novel bayes’ theorem for upper probabilities
Michele Caprio, Yusuf Sale, Eyke Hüllermeier, and Insup Lee · 2024
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Hyper-diffusion: Estimating epistemic and aleatoric uncertainty with a single model
Matthew A Chan, Maria J Molina, and Christopher A Metzler · 2024
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Quantifying aleatoric and epistemic uncertainty with proper scoring rules
Paul Hofman, Yusuf Sale, and Eyke Hüllermeier · 2024
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Dirichlet-based uncertainty quantification for personalized federated learning with improved posterior networks
Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takac, and Maxim Panov · 2024
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