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Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification.
Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management
S. C. Hora · 1996
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Statistical learning theory: Models, concepts, and results
U. Von Luxburg and B. Schölkopf · 2011
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A generalized bias-variance decomposition for bregman divergences
D. Pfau · 2013
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Why m heads are better than one: Training a diverse ensemble of deep networks
S. Lee, S. Purushwalkam, M. Cogswell, D. Crandall, and D. Batra · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
S. Depeweg, J.-M. Hernandez-Lobato, F. Doshi-Velez, and S. Udluft · 2018
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Bias-reduced uncertainty estimation for deep neural classifiers
Y. Geifman, G. Uziel, and R. El-Yaniv · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
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Evidential deep learning to quantify classification uncertainty
M. Sensoy, L. Kaplan, and M. Kandemir · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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A simple baseline for bayesian uncertainty in deep learning
W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson · 2019
Cited alongside, same era.
Measuring calibration in deep learning
J. Nixon, M. W. Dusenberry, L. Zhang, G. Jerfel, and D. Tran · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek · 2019
Cited alongside, same era.
Human uncertainty makes classification more robust
J. C. Peterson, R. M. Battleday, T. L. Griffiths, and O. Russakovsky · 2019
Cited alongside, same era.
Pytorch image models
R. Wightman · 2019
Cited alongside, same era.
Learning loss for active learning
D. Yoo and I. S. Kweon · 2019
Cited alongside, same era.
On the practicality of deterministic epistemic uncertainty
J. Postels, M. Segù, T. Sun, L. D. Sieber, L. Van Gool, F. Yu, and F. Tombari · 2022
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Is one annotation enough?-a data-centric image classification benchmark for noisy and ambiguous label estimation
L. Schmarje, V. Grossmann, C. Zelenka, S. Dippel, R. Kiko, M. Oszust, M. Pastell, J. Stracke, A. Valros, N. Volkmann, et al · 2022
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Plex: Towards reliability using pretrained large model extensions
D. Tran, J. Z. Liu, M. W. Dusenberry, D. Phan, M. Collier, J. Ren, K. Han, Z. Wang, Z. E. Mariet, H. Hu, N. Band, T. G. J. Rudner, Z. Nado, J. van Amersfoort, A. Kirsch, R. Jenatton, N. Thain, E. K. Buchanan, K. P. Murphy, D. Sculley, Y. Gal, Z. Ghahramani, J. Snoek, and B. Lakshminarayanan · 2022
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A deeper look into aleatoric and epistemic uncertainty disentanglement
M. Valdenegro-Toro and D. S. Mori · 2022
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Learning with noisy labels revisited: A study using real-world human annotations
J. Wei, Z. Zhu, H. Cheng, T. Liu, G. Niu, and Y. Liu · 2022
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L. Beyer, O. J. Hénaff, A. Kolesnikov, X. Zhai, and A. v. d. Oord · 2020
Cited alongside, same era.
Experiment tracking with weights and biases, 2020
L. Biewald · 2020
Cited alongside, same era.
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
B. Charpentier, D. Zügner, and S. Günnemann · 2020
Cited alongside, same era.
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
J. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax Weiss, and B. Lakshminarayanan · 2020
Cited alongside, same era.
Uncertainty estimation using a single deep deterministic neural network
J. Van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal · 2020
Cited alongside, same era.
Large batch optimization for deep learning: Training bert in 76 minutes
Y. You, J. Li, S. Reddi, J. Hseu, S. Kumar, S. Bhojanapalli, X. Song, J. Demmel, K. Keutzer, and C.-J. Hsieh · 2020
Cited alongside, same era.
On second-order scoring rules for epistemic uncertainty quantification
V. Bengs, E. Hüllermeier, and W. Waegeman · 2023
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Massively scaling heteroscedastic classifiers
M. Collier, R. Jenatton, B. Mustafa, N. Houlsby, J. Berent, and E. Kokiopoulou · 2023
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Sources of uncertainty in machine learning–a statisticians’ view
C. Gruber, P. O. Schenk, M. Schierholz, F. Kreuter, and G. Kauermann · 2023
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Uncertainty estimates of predictions via a general bias-variance decomposition
S. Gruber and F. Buettner · 2023
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DEUP: Direct epistemic uncertainty prediction
S. Lahlou, M. Jain, H. Nekoei, V. I. Butoi, P. Bertin, J. Rector-Brooks, M. Korablyov, and Y. Bengio · 2023
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B. Mucsányi, M. Kirchhof, E. Nguyen, A. Rubinstein, and S. J. Oh · 2023
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Deep deterministic uncertainty: A new simple baseline
J. Mukhoti, A. Kirsch, J. van Amersfoort, P. H. Torr, and Y. Gal · 2023
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Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation
D. Ulmer, C. Hardmeier, and J. Frellsen · 2023
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Likelihood annealing: Fast calibrated uncertainty for regression
U. Upadhyay, J. M. Kim, C. Schmidt, B. Schölkopf, and Z. Akata · 2023
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Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
L. Wimmer, Y. Sale, P. Hofman, B. Bischl, and E. Hüllermeier · 2023
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An introduction to neural data compression
Y. Yang, S. Mandt, L. Theis, et al · 2023
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How disentangled are your classification uncertainties?
I. P. de Jong, A. I. Sburlea, and M. Valdenegro-Toro · 2024
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Pretrained visual uncertainties
M. Kirchhof, M. Collier, S. J. Oh, and E. Kasneci · 2024
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