1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
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…
cs.LG2023
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…