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Underlying the use of statistical approaches for a wide range of applications is the assumption that the probabilities obtained from a statistical model are representative of the "true" probability that event, or outcome, will occur.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Using bayesian model averaging to calibrate forecast ensembles
Adrian E Raftery, Tilmann Gneiting, Fadoua Balabdaoui, and Michael Polakowski · 2005
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
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Estimating reliability and resolution of probability forecasts through decomposition of the empirical score
Jochen Bröcker · 2012
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Calibrating predictive model estimates to support personalized medicine
Xiaoqian Jiang, Melanie Osl, Jihoon Kim, and Lucila Ohno-Machado · 2012
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Accurate probability calibration for multiple classifiers
Wenliang Zhong and James T Kwok · 2013
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Calibrated structured prediction
Volodymyr Kuleshov and Percy S Liang · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht · 2015
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Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan O’Connor · 2015
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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Measuring calibration in deep learning
Jeremy Nixon, Michael W Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
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Calibrating deep convolutional gaussian processes
Gia-Lac Tran, Edwin V Bonilla, John Cunningham, Pietro Michiardi, and Maurizio Filippone · 2019
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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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Calibration tests in multi-class classification: A unifying framework
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2019
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
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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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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 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
Cited alongside, same era.
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
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Neural network calibration for medical imaging classification using dca regularization
Gongbo Liang, Yu Zhang, and Nathan Jacobs · 2020
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Uncertainty quantification and deep ensembles
Rahul Rahaman and Alexandre H Thiery · 2020
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Diverse ensembles improve calibration
Asa Cooper Stickland and Iain Murray · 2020
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Confidence-calibrated adversarial training: Generalizing to unseen attacks
David Stutz, Matthias Hein, and Bernt Schiele · 2020
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Improving calibration of batchensemble with data augmentation
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, and Dustin Tran · 2020
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