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Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence.
Verification of forecasts expressed in terms of probability
G. W. Brier · 1950
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Reliability of subjective probability forecasts of precipitation and temperature
A. H. Murphy and R. L. Winkler · 1977
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The comparison and evaluation of forecasters
M. H. DeGroot and S. E. Fienberg · 1983
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Probabilities for SV machines
J. Platt · 2000
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
B. Zadrozny and C. Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
B. Zadrozny and C. Elkan · 2002
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Improving the AUC of probabilistic estimation trees
C. Ferri, P. A. Flach, and J. Hernández-Orallo · 2003
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Statistical comparisons of classifiers over multiple data sets
J. Demšar · 2006
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
M. Kull and P. Flach · 2015
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Obtaining well calibrated probabilities using bayesian binning
M. P. Naeini, G. Cooper, and M. Hauskrecht · 2015
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Densely connected convolutional networks
G. Huang, Z. Liu, and K. Q. Weinberger · 2016
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
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Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
M. Kull, T. M. Silva Filho, and P. Flach · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, and S. Wanderman-Milne · 2018
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Accurate uncertainties for deep learning using calibrated regression
V. Kuleshov, N. Fenner, and S. Ermon · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
A. Kumar, S. Sarawagi, and U. Jain · 2018
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Dirichlet-based gaussian processes for large-scale calibrated classification
D. Milios, R. Camoriano, P. Michiardi, L. Rosasco, and M. Filippone · 2018
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Binary classifier calibration using an ensemble of near isotonic regression models
M. P. Naeini and G. F. Cooper · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Base pretrained models and datasets in pytorch, 2017
A. Cheni · 2017
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On Calibration of Modern Neural Networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Non-parametric Bayesian isotonic calibration: Fighting over-confidence in binary classification
M.-L. Allikivi and M. Kull · 2019
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Verified uncertainty calibration
A. Kumar, P. Liang, and T. Ma · 2019
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A simple baseline for bayesian uncertainty in deep learning
W. Maddox, T. Garipov, P. Izmailov, D. P. Vetrov, and A. G. Wilson · 2019
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Evaluating model calibration in classification
J. Vaicenavicius, D. Widmann, C. Andersson, F. Lindsten, J. Roll, and T. Schön · 2019
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