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When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty.
The proof and measurement of association between two things
Spearman, C. (1904) · 1904
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DCTD: deep conditional target densities for accurate regression
Gustafsson, F. K., Danelljan, M., Bhat, G., & Schön, T. B. (2019) · 1909
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Kalman, R. E. (1960) · 1960
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A., & Caruana, R. (2005) · 2005
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Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T., & Raftery, A. E. (2007) · 2007
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The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · 2009
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Learning a confidence measure for optical flow
Mac Aodha, O., Humayun, A., Pollefeys, M., & Brostow, G. J. (2013) · 2012
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Adam: A method for stochastic optimization
Kingma, D., & Ba, J. (2015) · 2015
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Obtaining well calibrated probabilities using bayesian binning
Pakdaman Naeini, M., Cooper, G., & Hauskrecht, M. (2015) · 2015
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Introspective classification for robot perception
Grimmett, H., Triebel, R., Paul, R., & Posner, I. (2016) · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A., & Gal, Y. (2017) · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017) · 2017
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Probflow: Joint optical flow and uncertainty estimation
Wannenwetsch, A. S., Keuper, M., & Roth, S. (2017) · 2017
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Uncertainty estimates and multi-hypotheses networks for optical flow
Wasserstein distances for stereo disparity estimation
Garg, D., Wang, Y., Hariharan, B., Campbell, M., Weinberger, K. Q., & Chao, W.-L. (2020) · 2020
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A general framework for uncertainty estimation in deep learning
Loquercio, A., Segu, M., & Scaramuzza, D. (2020) · 2020
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Predicting disparity distributions
Häger, G., Persson, M., & Felsberg, M. (2021) · 2021
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NOMU: Neural optimization-based model uncertainty
Heiss, J., Weissteiner, J., Wutte, H., Seuken, S., & Teichmann, J. (2022) · 2022
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Estimating uncertainty in neural networks for cardiac mri segmentation: A benchmark study
Ng, M., Guo, F., Biswas, L., Petersen, S. E., Piechnik, S. K., Neubauer, S., & Wright, G. (2023) · 2022
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Ilg, E., Cicek, O., Galesso, S., Klein, A., Makansi, O., Hutter, F., & Brox, T. (2018) · 2018
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Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., & Ermon, S. (2018) · 2018
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., & Snoek, J. (2019) · 2019
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Learning loss for active learning
Yoo, D., & Kweon, I. S. (2019) · 2019
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Revised md17 dataset (rmd17)
Christensen, A., Anders S.; Von Lilienfeld (2020) · 2020
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Uncertainty-aware CNNs for depth completion: Uncertainty from beginning to end
Eldesokey, A., Felsberg, M., Holmquist, K., & Persson, M. (2020) · 2020
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Gawlikowski, J., Rovile Njieutcheu Tassi, C., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shahzad, M., Yang, W., Bamler, R., & Zhu, X. X. (2023) · 2023
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Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Tan, A. R., Urata, S., Goldman, S., Dietschreit, J. C. B., & Gómez-Bombarelli, R. (2023) · 2023
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Learning sample difficulty from pre-trained models for reliable prediction
Cui, P., Zhang, D., Deng, Z., Dong, Y., & Zhu, J. (2024) · 2024
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A simple approach to improve single-model deep uncertainty via distance-awareness
Liu, J. Z., Padhy, S., Ren, J., Lin, Z., Wen, Y., Jerfel, G., Nado, Z., Snoek, J., Tran, D., & Lakshminarayanan, B. (2024) · 2024
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Hinge-wasserstein: Mitigating overconfidence in regression by classification
Xiong, Z., Eldesokey, A., Johnander, J., Wandt, B., & Forssén, P.-E. (2024) · 2024
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