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Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
A method for numerical integration on an automatic computer
Charles W Clenshaw and Alan R Curtis · 1960
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On the limited memory bfgs method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt · 1999
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Bianca Zadrozny and Charles Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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3d object representations for fine-grained categorization
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Adam: A method for stochastic optimization
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Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Deep residual learning for image recognition
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Verified uncertainty calibration
Ananya Kumar, Percy Liang, and Tengyu Ma · 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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Unsupervised temperature scaling: Post-processing unsupervised calibration of deep models decisions
Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão, and Christian Gagné · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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Measuring calibration in deep learning
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
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Alex Kendall and Yarin Gal · 2017
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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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Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
Meelis Kull, Telmo M Silva Filho, Peter Flach, et al · 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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Seonguk Seo, Paul Hongsuck Seo, and Bohyung Han · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
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Unconstrained monotonic neural networks
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Cutmix: Regularization strategy to train strong classifiers with localizable features
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
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Verified uncertainty calibration
Ananya Kumar, Percy Liang, and Tengyu Ma · 2019
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Measuring calibration in deep learning
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Unconstrained monotonic neural networks
Antoine Wehenkel and Gilles Louppe · 2019
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Distance-based learning from errors for confidence calibration
Chen Xing, Sercan Arik, Zizhao Zhang, and Tomas Pfister · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Jize Zhang, Bhavya Kailkhura, and T Han · 2020
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