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Aleatoric (data) and epistemic (knowledge) uncertainty are textbook components of Uncertainty Quantification.
Bci competition 2008–graz data set a
Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., and Pfurtscheller, G · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Dropout training as adaptive regularization
Wager, S., Wang, S., and Liang, P. S · 2013
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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., et al · 2015
Earlier work this paper cites.
Training deep networks for facial expression recognition with crowd-sourced label distribution
Barsoum, E., Zhang, C., Canton Ferrer, C., and Zhang, Z · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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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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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
Earlier work this paper cites.
Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
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Understanding measures of uncertainty for adversarial example detection
Smith, L. and Gal, Y · 2018
Cited alongside, same era.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Wen, Y., Vicol, P., Ba, J., Tran, D., and Grosse, R · 2018
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 2019
Cited alongside, same era.
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al · 2021
Cited alongside, same era.
Massively scaling heteroscedastic classifiers
Collier, M., Jenatton, R., Mustafa, B., Houlsby, N., Berent, J., and Kokiopoulou, E · 2023
Later among the works it cites.
DEUP: Direct epistemic uncertainty prediction
Lahlou, S., Jain, M., Nekoei, H., Butoi, V. I., Bertin, P., Rector-Brooks, J., Korablyov, M., and Bengio, Y · 2023
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Deep deterministic uncertainty: A new simple baseline
Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P. H., and Gal, Y · 2023
Later among the works it cites.
Introducing an improved information-theoretic measure of predictive uncertainty
Schweighofer, K., Aichberger, L., Ielanskyi, M., and Hochreiter, S · 2023
Later among the works it cites.
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
Wimmer, L., Sale, Y., Hofman, P., Bischl, B., and Hüllermeier, E · 2023
Later among the works it cites.
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Multicenter validation of a deep learning detection algorithm for focal cortical dysplasia
Gill, R. S., Lee, H.-M., Caldairou, B., Hong, S.-J., Barba, C., Deleo, F., d’Incerti, L., Mendes Coelho, V. C., Lenge, M., Semmelroch, M., et al · 2021
Cited alongside, same era.
Dropconnect is effective in modeling uncertainty of bayesian deep networks
Mobiny, A., Yuan, P., Moulik, S. K., Garg, N., Wu, C. C., and Van Nguyen, H · 2021
Cited alongside, same era.
Exploring the limits of epistemic uncertainty quantification in low-shot settings
Valdenegro-Toro, M · 2021
Cited alongside, same era.
Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms
Vranken, J. F., van de Leur, R. R., Gupta, D. K., Juarez Orozco, L. E., Hassink, R. J., van der Harst, P., Doevendans, P. A., Gulshad, S., and van Es, R · 2021
Cited alongside, same era.
On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks
Seitzer, M., Tavakoli, A., Antic, D., and Martius, G · 2022
Cited alongside, same era.
A deeper look into aleatoric and epistemic uncertainty disentanglement
Valdenegro-Toro, M. and Mori, D. S · 2022
Cited alongside, same era.
Certainty about uncertainty in sleep staging: a theoretical framework
van Gorp, H., Huijben, I. A., Fonseca, P., van Sloun, R. J., Overeem, S., and van Gilst, M. M · 2022
Cited alongside, same era.
Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram
Barandas, M., Famiglini, L., Campagner, A., Folgado, D., Simão, R., Cabitza, F., and Gamboa, H · 2024
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Quantifying aleatoric and epistemic uncertainty: A credal approach
Hofman, P., Sale, Y., and Hüllermeier, E · 2024
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Predictive uncertainty quantification via risk decompositions for strictly proper scoring rules
Kotelevskii, N. and Panov, M · 2024
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Uncertainty quantification for cross-subject motor imagery classification
Manivannan, P., de Jong, I. P., Valdenegro-Toro, M., and Sburlea, A. I · 2024
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Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks
Mucsányi, B., Kirchhof, M., and Oh, S. J · 2024
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Are uncertainty quantification capabilities of evidential deep learning a mirage?
Shen, M., Ryu, J. J., Ghosh, S., Bu, Y., Sattigeri, P., Das, S., and Wornell, G · 2024
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Position: Uncertainty quantification needs reassessment for large language model agents
Kirchhof, M., Kasneci, G., and Kasneci, E · 2025
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