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Uncertainty quantification in machine learning applications

Schmerler, S.; Starke, S.; Steinbach, P.; M. K. Siddiqui, Q.; Fiedler, L.; Cangi, A.; Kulkarni, S. H.

We strive to popularize the usage of uncertainty quantification methods in machine learning through publications and application in various projects covering diverse fields from regression and classification to instance segmentation. In addition, we employ domain shift detection techniques to tackle population-level out-of-distribution scenarios. In all cases, the goal is to assess model prediction validity given unseen test data.

Keywords: machine learning; uncertainty

  • Open Access Logo Poster
    Helmholtz AI Evaluation 2022, 05.-07.10.2022, München, Germany

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Permalink: https://www.hzdr.de/publications/Publ-35454
Publ.-Id: 35454