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In healthcare applications, predictive uncertainty has been used to assess predictive accuracy.
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems (1989)
1989
Earlier work this paper cites.
Nix, D.A., Weigend, A.S.: Estimating the mean and variance of the target probability distribution. IEEE International Conference on Neural Networks (1994)
1994
Earlier work this paper cites.
Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. Journal of the Royal Statistical Society (2001)
2001
Earlier work this paper cites.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing (2004)
2004
Earlier work this paper cites.
Joint Committee for Guides in Metrology: Jcgm 100:2008. evaluation of measurement data - guide to the expression of uncertainty in measurement. Technical report (2008)
2008
Earlier work this paper cites.
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Springer, New York (2009)
2009
Earlier work this paper cites.
Kiureghian, A.D., Ditlevsen, O.: Aleatory or epistemic? does it matter? Structural Safety (2009)
2009
Earlier work this paper cites.
Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. MICCAI (2012)
2012
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. JMLR (2014)
2014
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. ICLR (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ICLR (2015)
2015
Cited alongside, same era.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. MICCAI (2015)
2015
Cited alongside, same era.
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. ICML (2016)
2016
Cited alongside, same era.
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press (2016)
2016
Cited alongside, same era.
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. CVPR (2016)
2016
Cited alongside, same era.
Adler, J., Öktem, O.: Deep bayesian inversion. arXiv preprint arXiv:1811.05910 (2018)
2018
Later among the works it cites.
Depeweg, S., Hernández-Lobato, J.M., Doshi-Velez, F., Udluft, S.: Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning. ICML (2018)
2018
Later among the works it cites.
Kuleshov, V., Fenner, N., Ermon, S.: Accurate uncertainties for deep learning using calibrated regression. ICML (2018)
2018
Later among the works it cites.
Zbontar, J., Knoll, F., Sriram, A., Muckley, M.J., Bruno, M., Defazio, A., Parente, M., Geras, K.J., Katsnelson, J., Chandarana, H., Zhang, Z., Drozdzal, M., Romero, A., Rabbat, M., Vincent, P., Pinkerton, J., Wang, D., Yakubova, N., Owens, E., Zitnick, C.L., Recht, M.P., Sodickson, D.K., Lui, Y.W.: fastmri: An open dataset and benchmarks for accelerated mri. arXiv preprint arXiv: 1811.08839 (2018)
2018
Later among the works it cites.
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Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. ICML (2017)
2017
Cited alongside, same era.
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? NIPS (2017)
2017
Cited alongside, same era.
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. NIPS (2017)
2017
Cited alongside, same era.
Tanno, R., Worrall, D.E., Ghosh, A., Kaden, E., Sotiropoulos, S.N., Criminisi, A., Alexander, D.C.: Bayesian image quality transfer with cnns: Exploring uncertainty in dmri super-resolution. MICCAI (2017)
2017
Cited alongside, same era.
Valindria, V.V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B.: Reverse classification accuracy: Predicting segmentation performance in the absence of ground truth. IEEE Transactions on Medical Imaging (2017)
2017
Cited alongside, same era.
Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthcare Journal (2019)
2019
Later among the works it cites.
Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., Welling, M.: Supervised uncertainty quantification for segmentation with multiple annotations. MICCAI (2019)
2019
Later among the works it cites.
Kwon, Y., Won, J.H., Kim, B.J., Paik, M.C.: Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation. Computational Statistics and Data Analysis (2019)
2019
Later among the works it cites.
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J.V., Lakshminarayanan, B., Snoek, J.: Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift. NeurIPS (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Pezzotti, N., Yousefi, S., Elmahdy, M.S., Gemert, J.H.F.V., Schuelke, C., Doneva, M., Nielsen, T., Kastryulin, S., Lelieveldt, B.P., Osch, M.J.V., Weerdt, E.D., Staring, M.: An adaptive intelligence algorithm for undersampled knee mri reconstruction. IEEE Access (2020)
2020
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