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Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves.
Mixture density networks
Bishop, C. M. 1994 · 1994
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J.; et al. 1999 · 1999
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Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B.; and Elkan, C. 2002 · 2002
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Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Rahimi, A.; Shaban, A.; Cheng, C.-A.; Boots, B.; and Hartley, R. 2020 · 2003
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Loss functions for binary class probability estimation and classification: Structure and applications
Buja, A.; Stuetzle, W.; and Shen, Y. 2005 · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, A. 2009 · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X.; and Bengio, Y. 2010 · 2010
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Composite binary losses
Reid, M. D.; and Williamson, R. C. 2010 · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Net2net: Accelerating learning via knowledge transfer
Chen, T.; Goodfellow, I.; and Shlens, J. 2015 · 2015
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Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P.; Cooper, G.; and Hauskrecht, M. 2015 · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Deep Networks with Stochastic Depth
Densely connected convolutional networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
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Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Kull, M.; Silva Filho, T.; and Flach, P. 2017 · 2017
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Mixup: Beyond Empirical Risk Minimization
Zhang, H.; Cissé, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2018 · 2018
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Kull, M.; Nieto, M. P.; Kängsepp, M.; Silva Filho, T.; Song, H.; and Flach, P. 2019 · 2019
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Verified uncertainty calibration
Kumar, A.; Liang, P. S.; and Ma, T. 2019 · 2019
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When does label smoothing help?
Müller, R.; Kornblith, S.; and Hinton, G. E. 2019 · 2019
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Huang, G.; Sun, Y.; Liu, Z.; Sedra, D.; and Weinberger, K. 2016 · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
Cited alongside, same era.
On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
Cited alongside, same era.
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Calibration of Neural Networks using Splines
Gupta, K.; Rahimi, A.; Ajanthan, T.; Mensink, T.; Sminchisescu, C.; and Hartley, R. 2021 · 2021
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