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We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS).
Murphy, A.H.: A new vector partition of the probability score. Journal of Applied Meteorology and Climatology 12
1973
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: ADVANCES IN LARGE MARGIN CLASSIFIERS. pp. 61–74. MIT Press (1999)
1999
Earlier work this paper cites.
Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In: Icml. vol. 1, pp. 609–616. Citeseer (2001)
2001
Earlier work this paper cites.
Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 694–699 (2002)
2002
Earlier work this paper cites.
Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association 102
2007
Earlier work this paper cites.
2014
Earlier work this paper cites.
Naeini, M.P., Cooper, G., Hauskrecht, M.: Obtaining well calibrated probabilities using bayesian binning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1251–1258 (2017)
2017
Cited alongside, same era.
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1321–1330. JMLR. org (2017)
2017
Cited alongside, same era.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
Rahimi, A., Shaban, A., Cheng, C.A., Hartley, R., Boots, B.: Intra order-preserving functions for calibration of multi-class neural networks. Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
Wenger, J., Kjellström, H., Triebel, R.: Non-parametric calibration for classification. In: International Conference on Artificial Intelligence and Statistics. pp. 178–190. PMLR (2020)
2020
Later among the works it cites.
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Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4510–4520 (2018)
2018
Cited alongside, same era.
Wan, S., Wu, T.Y., Wong, W.H., Lee, C.Y.: Confnet: Predict with confidence. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2921–2925 (2018)
2018
Cited alongside, same era.
Kumar, A., Liang, P.S., Ma, T.: Verified uncertainty calibration. In: Advances in Neural Information Processing Systems. pp. 3792–3803 (2019)
2019
Cited alongside, same era.
Nixon, J., Dusenberry, M.W., Zhang, L., Jerfel, G., Tran, D.: Measuring calibration in deep learning. In: CVPR Workshops. vol. 2 (2019)
2019
Cited alongside, same era.
Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Roll, J., Schön, T.: Evaluating model calibration in classification. In: The 22nd International Conference on Artificial Intelligence and Statistics. pp. 3459–3467. PMLR (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Jang, S., Lee, I., Weimer, J.: Improving classifier confidence using lossy label-invariant transformations. In: International Conference on Artificial Intelligence and Statistics. pp. 4051–4059. PMLR (2021)
2021
Closest in time.
Ma, X., Blaschko, M.B.: Meta-cal: Well-controlled post-hoc calibration by ranking. In: International Conference on Machine Learning. pp. 7235–7245. PMLR (2021)
2021
Closest in time.
Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., Lucic, M.: Revisiting the calibration of modern neural networks. Advances in Neural Information Processing Systems 34
2021
Closest in time.