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In many classification applications, the prediction of a deep neural network (DNN) based classifier needs to be accompanied by some confidence indication.
The comparison and evaluation of forecasters
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Are humans good intuitive statisticians after all? rethinking some conclusions from the literature on judgment under uncertainty
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Inductive confidence machines for regression
Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A · 2002
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Transforming classifier scores into accurate multiclass probability estimates
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The elements of statistical learning: data mining, inference and prediction
Hastie, T., Tibshirani, R., Friedman, J., and Franklin, J · 2005
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
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Algorithmic learning in a random world , volume 29
Vovk, V., Gammerman, A., and Shafer, G · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Conditional validity of inductive conformal predictors
Vovk, V · 2012
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Distribution-free prediction bands for non-parametric regression
Lei, J. and Wasserman, L · 2014
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Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
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Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., and Dudley, J. T · 2018
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Uncertainty sets for image classifiers using conformal prediction
Angelopoulos, A. N., Bates, S., Jordan, M., and Malik, J · 2021
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Local temperature scaling for probability calibration
Ding, Z., Han, X., Liu, P., and Niethammer, M · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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The limits of distribution-free conditional predictive inference
Foygel Barber, R., Candes, E. J., Ramdas, A., and Tibshirani, R. J · 2021
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Network calibration by class-based temperature scaling
Frenkel, L. and Goldberger, J · 2021
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Rethinking calibration of deep neural networks: Do not be afraid of overconfidence
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Bin-wise temperature scaling (bts): Improvement in confidence calibration performance through simple scaling techniques
Ji, B., Jung, H., Yoon, J., Kim, K., et al · 2019
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Measuring calibration in deep learning
Nixon, J., Dusenberry, M. W., Zhang, L., Jerfel, G., and Tran, D · 2019
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Least ambiguous set-valued classifiers with bounded error levels
Sadinle, M., Lei, J., and Wasserman, L · 2019
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Conformal prediction under covariate shift
Tibshirani, R. J., Foygel Barber, R., Candes, E., and Ramdas, A · 2019
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A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., and Macesanu, G · 2020
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Distribution-free binary classification: prediction sets, confidence intervals and calibration
Gupta, C., Podkopaev, A., and Ramdas, A · 2020
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Wang, D.-B., Feng, L., and Zhang, M.-L · 2021
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Lu, C., Ahmed, S. R., Singh, P., and Kalpathy-Cramer, J · 2022
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Learning optimal conformal classifiers
Stutz, D., Cemgil, A. T., Doucet, A., et al · 2022
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Mitigating neural network overconfidence with logit normalization
Wei, H., Xie, R., Cheng, H., Feng, L., An, B., and Li, Y · 2022
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Conformal prediction beyond exchangeability
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J · 2023
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Class-conditional conformal prediction with many classes
Ding, T., Angelopoulos, A., Bates, S., Jordan, M., and Tibshirani, R. J · 2023
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Conformal prediction with conditional guarantees
Gibbs, I., Cherian, J. J., and Candès, E. J · 2023
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Federated conformal predictors for distributed uncertainty quantification
Lu, C., Yu, Y., Karimireddy, S. P., Jordan, M., and Raskar, R · 2023
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Does confidence calibration improve conformal prediction?
Xi, H., Huang, J., Liu, K., Feng, L., and Wei, H · 2024
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