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Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W., Garipov, T., Izmailov, P., Vetrov, D. P., and Wilson, A. G · 1902
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Sampling-free epistemic uncertainty estimation using approximated variance propagation
Postels, J., Ferroni, F., Coskun, H., Navab, N., and Tombari, F · 1908
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J · 1999
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Zadrozny, B. and Elkan, C · 2001
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Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R. B., and He, K · 2003
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Loss functions for binary class probability estimation and classification: Structure and applications,” manuscript, available at www-stat.wharton.upenn.edu/ buja, 2005
Buja, A., Stuetzle, W., and Shen, Y · 2005
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Weather prediction by numerical process
Richardson, L. F · 2007
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Shamout, F. E., Shen, Y., Wu, N., Kaku, A., Park, J., Makino, T., Jastrzebski, S., Wang, D., Zhang, B., Dogra, S., Cao, M., Razavian, N., Kudlowitz, D., Azour, L., Moore, W., Lui, Y. W., Aphinyanaphongs, Y., Fernandez-Granda, C., and Geras, K. J · 2008
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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 · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Machine learning : a probabilistic perspective
Murphy, K. P · 2013
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning, 2016
Gal, Y. and Ghahramani, Z · 2016
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Deep learning , volume 1
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Natural-parameter networks: A class of probabilistic neural networks
Wang, H., Shi, X., and Yeung, D · 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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Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
Kull, M., Filho, T. M. S., and Flach, P · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles, 2017
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, L., and Hinton, G. E · 2017
Cited alongside, same era.
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression, 2018
Candes, E. J. and Sur, P · 2018
Cited alongside, same era.
Attention-based deep multiple instance learning
Ilse, M., Tomczak, J., and Welling, M · 2018
Cited alongside, same era.
Rainnet: a convolutional neural network for radar-based precipitation nowcasting
Ayzel, G · 2020
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Improved trainable calibration method for neural networks on medical imaging classification
Liang, G., Zhang, Y., Wang, X., and Jacobs, N · 2020
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Early-learning regularization prevents memorization of noisy labels
Liu, S., Niles-Weed, J., Razavian, N., and Fernandez-Granda, C · 2020
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Calibrating deep neural networks using focal loss
Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P. H. S., and Dokania, P · 2020
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Can you trust predictive uncertainty under real dataset shifts in digital pathology?
Thagaard, J., Hauberg, S., van der Vegt, B., Ebstrup, T., Hansen, J. D., and Dahl, A. B · 2020
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Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., and Ermon, S · 2018
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.
Deephit: A deep learning approach to survival analysis with competing risks
Lee, C., Zame, W., Yoon, J., and van der Schaar, M · 2018
Cited alongside, same era.
The implicit bias of gradient descent on separable data, 2018
Soudry, D., Hoffer, E., Nacson, M. S., Gunasekar, S., and Srebro, N · 2018
Cited alongside, same era.
High Dimensional Probability
Vershynin, R · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H., Cissé, M., Dauphin, Y., and Lopez-Paz, D · 2018
Cited alongside, same era.
Machine learning for precipitation nowcasting from radar images
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J · 2019
Cited alongside, same era.
On mixup training: Improved calibration and predictive uncertainty for deep neural networks, 2020
Thulasidasan, S., Chennupati, G., Bilmes, J., Bhattacharya, T., and Michalak, S · 2020
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Deep learning-based survival prediction for multiple cancer types using histopathology images
Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament, I., Mermel, C., Chen, P.-H. C., Liu, Y., and Stumpe, M. C · 2020
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Robust early-learning: Hindering the memorization of noisy labels
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., and Chang, Y · 2020
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Searching to exploit memorization effect in learning with noisy labels
Yao, Q., Yang, H., Han, B., Niu, G., and Kwok, J. T.-Y · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Zhang, J., Kailkhura, B., and Han, T. Y · 2020
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X-cal: Explicit calibration for survival analysis, 2021
Goldstein, M., Han, X., Puli, A., Perotte, A. J., and Ranganath, R · 2021
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Calibration of neural networks using splines
Gupta, K., Rahimi, A., Ajanthan, T., Mensink, T., Sminchisescu, C., and Hartley, R · 2021
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Hüllermeier, E. and Waegeman, W · 2021
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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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Skillful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al · 2021
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Right decisions from wrong predictions: A mechanism design alternative to individual calibration
Zhao, S. and Ermon, S · 2021
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