Uncertainty-Aware Evaluation of Quantitative ML Safety Requirements
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-68738-9_31
Reference17 articles.
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3. Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78(1), 1–3 (1950)
4. Burton, S., Hellert, C., Hüger, F., Mock, M., Rohatschek, A.: Safety assurance of machine learning for perception functions. In: Fingscheidt, T., Gottschalk, H., Houben, S. (eds.) Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety, pp. 335–358. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-01233-4_12
5. Burton, S., Herd, B.: Addressing uncertainty in the safety assurance of machine-learning. Frontiers Comput. Sci. 5, 1132580 (2023). https://doi.org/10.3389/fcomp.2023.1132580
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