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Recent works have shown that most deep learning models are often poorly calibrated, i.e., they may produce overconfident predictions that are wrong.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
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Allan H Murphy · 1972
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
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Song Xi Chen · 1999
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Transforming classifier scores into accurate multiclass probability estimates
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Cumulative residual entropy: a new measure of information
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Properties and benefits of calibrated classifiers
Ira Cohen and Moises Goldszmidt · 2004
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Tilmann Gneiting and Adrian E Raftery · 2007
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Probabilistic forecasts, calibration and sharpness
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Pav and the roc convex hull
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Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
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Calibrated prediction intervals for neural network regressors
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Verified uncertainty calibration
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Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas B Schön · 2019
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, and Peter Flach · 2019
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Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo Silva Filho, and Peter Flach · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 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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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Distribution Calibration for Regression
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Stochastic optimization of sorting networks via continuous relaxations
Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon · 2019
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