30 citations · 32 across the 5 of their papers we have counts for
3 papers · 1 filter
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice
Lisa Jöckel, Michael Kläs, Georg Popp +2
When systems use data-based models that are based on machine learning (ML), errors in their results cannot be ruled out. This is particularly critical if it remains unclear to the…
Badgers: generating data quality deficits with Python
Julien Siebert, Daniel Seifert, Patricia Kelbert +2
Generating context specific data quality deficits is necessary to experimentally assess data quality of data-driven (artificial intelligence (AI) or machine learning (ML)) applicat…
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning
Janek Groß, Michael Kläs, Lisa Jöckel +1
As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models…