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With model trustworthiness being crucial for sensitive real-world applications, practitioners are putting more and more focus on improving the uncertainty calibration of deep neural networks.
A Class of Statistics with Asymptotically Normal Distribution
Hoeffding, W · 1948
Earlier work this paper cites.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
Earlier work this paper cites.
A new vector partition of the probability score
Murphy, A. H · 1973
Earlier work this paper cites.
Logistic-normal distributions: Some properties and uses
Aitchison, J. and Shen, S. M · 1980
Earlier work this paper cites.
The well-calibrated bayesian
Dawid, A. P · 1982
Earlier work this paper cites.
Advanced econometrics
Takeshi, A · 1985
Earlier work this paper cites.
Multivariate adaptive regression splines
Friedman, J. H · 1991
Earlier work this paper cites.
Mixture density networks
Bishop, C. M · 1994
Earlier work this paper cites.
Classification by pairwise coupling
Hastie, T. and Tibshirani, R · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Coherent dispersion criteria for optimal experimental design
Dawid, A. P. and Sebastiani, P · 1999
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. C · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Zadrozny, B. and Elkan, C · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C · 2002
Earlier work this paper cites.
Measure, integral and probability , volume 14
Capiński, M. and Kopp, P. E · 2004
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
Earlier work this paper cites.
Reliability, sufficiency, and the decomposition of proper scores
Bröcker, J · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Probabilistic forecasting
Gneiting, T. and Katzfuss, M · 2014
Earlier work this paper cites.
On the folded normal distribution
Tsagris, M., Beneki, C., and Hassani, H · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G. F., and Hauskrecht, M · 2015
Cited alongside, same era.
Posterior calibration and exploratory analysis for natural language processing models
Nguyen, K. and O’Connor, B · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
A novel machine learning model for estimation of sale prices of real estate units
Rafiei, M. H. and Adeli, H · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Being bayesian about categorical probability
Joo, T., Chung, U., and Seo, M · 2020
Later among the works it cites.
Being bayesian, even just a bit, fixes overconfidence in relu networks
Kristiadi, A., Hein, M., and Hennig, P · 2020
Later among the works it cites.
Intra order-preserving functions for calibration of multi-class neural networks
Rahimi, A., Shaban, A., Cheng, C.-A., Hartley, R., and Boots, B · 2020
Later among the works it cites.
Mastering atari, go, chess and shogi by planning with a learned model
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al · 2020
Later among the works it cites.
Non-parametric calibration for classification
Wenger, J., Kjellström, H., and Triebel, R · 2020
Later among the works it cites.
Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Kull, M., Filho, T. S., and Flach, P · 2017
Cited alongside, same era.
Trainable calibration measures for neural networks from kernel mean embeddings
Kumar, A., Sarawagi, S., and Jain, U · 2018
Cited alongside, same era.
Progressive neural architecture search
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., and Murphy, K · 2018
Cited alongside, same era.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Kull, M., Perello Nieto, M., Kängsepp, M., Silva Filho, T., Song, H., and Flach, P · 2019
Cited alongside, same era.
Zhang, J., Kailkhura, B., and Han, T. Y.-J · 2020
Later among the works it cites.
Beyond pinball loss: Quantile methods for calibrated uncertainty quantification, 2021
Chung, Y., Neiswanger, W., Char, I., and Schneider, J · 2021
Later among the works it cites.
Bert & family eat word salad: Experiments with text understanding
Gupta, A., Kvernadze, G., and Srikumar, V · 2021
Later among the works it cites.
A broad study on the transferability of visual representations with contrastive learning
Islam, A., Chen, C.-F., Panda, R., Karlinsky, L., Radke, R. J., and Feris, R. S · 2021
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Machine learning based disease prediction from genotype data
Katsaouni, N., Tashkandi, A., Wiese, L., and Schulz, M. H · 2021
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A statistical perspective on distillation
Menon, A. K., Rawat, A. S., Reddi, S. J., Kim, S., and Kumar, S · 2021
Later among the works it cites.
Revisiting the calibration of modern neural networks
Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., and Lucic, M · 2021
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Activation-level uncertainty in deep neural networks
Morales-Álvarez, P., Hernández-Lobato, D., Molina, R., and Hernández-Lobato, J. M · 2021
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A geometric perspective towards neural calibration via sensitivity decomposition
Tian, J., Yung, D., Hsu, Y.-C., and Kira, Z · 2021
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Post-hoc uncertainty calibration for domain drift scenarios
Tomani, C., Gruber, S., Erdem, M. E., Cremers, D., and Buettner, F · 2021
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Be confident! towards trustworthy graph neural networks via confidence calibration
Wang, X., Liu, H., Shi, C., and Yang, C · 2021
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Calibration tests beyond classification
Widmann, D., Lindsten, F., and Zachariah, D · 2021
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Accelerating bayesian neural networks via algorithmic and hardware optimizations
Fan, H., Ferianc, M., Que, Z., Niu, X., Rodrigues, M. L., and Luk, W · 2022
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A consistent and differentiable lp canonical calibration error estimator
Popordanoska, T., Sayer, R., and Blaschko, M. B · 2022
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On bias-variance alignment in deep models
Chen, L., Lukasik, M., Jitkrittum, W., You, C., and Kumar, S · 2024
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