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Confidence calibration is central to providing accurate and interpretable uncertainty estimates, especially under safety-critical scenarios.
Teoria statistica delle classi e calcolo delle probabilita
Carlo Bonferroni · 1936
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Verification of forecasts expressed in terms of probability
Glenn W Brier et al · 1950
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On estimation of a probability density function and mode
Emanuel Parzen · 1962
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A modified wilcoxon rank sum test for paired data
FC Lam and MT Longnecker · 1983
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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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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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statsmodels: Statistical modeling and econometrics in Python, 2009-
Jonathan Taylor · 2009
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Character-level convolutional networks for text classification
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 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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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
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Multicalibration: Calibration for the (Computationally-identifiable) masses
Ursula Hebert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
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To trust or not to trust a classifier
Heinrich Jiang, Been Kim, Melody Guan, and Maya Gupta · 2018
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
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Xcit: Cross-covariance image transformers
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Machine learning and surgical outcomes prediction: a systematic review
Omar Elfanagely, Yoshiko Toyoda, Sammy Othman, Joseph A Mellia, Marten Basta, Tony Liu, Konrad Kording, Lyle Ungar, and John P Fischer · 2021
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
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Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
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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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Dissecting racial bias in an algorithm used to manage the health of populations
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Learning robust global representations by penalizing local predictive power
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Pytorch image models
Ross Wightman · 2019
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Training data-efficient image transformers and distillation through attention
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Benchmarking representation learning for natural world image collections
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Beit: Bert pre-training of image transformers, 2022
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
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Bias in, bias out: underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection—a scoping review
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Calibrated and sharp uncertainties in deep learning via density estimation
Volodymyr Kuleshov and Shachi Deshpande · 2022
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Taking a step back with kcal: Multi-class kernel-based calibration for deep neural networks
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun · 2022
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Parameterized temperature scaling for boosting the expressive power in post-hoc uncertainty calibration
Christian Tomani, Daniel Cremers, and Florian Buettner · 2022
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Birds of a feather trust together: Knowing when to trust a classifier via adaptive neighborhood aggregation
Miao Xiong, Shen Li, Wenjie Feng, Ailin Deng, Jihai Zhang, and Bryan Hooi · 2022
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Beyond calibration: estimating the grouping loss of modern neural networks
Alexandre Perez-Lebel, Marine Le Morvan, and Gaël Varoquaux · 2023
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