Michael Kläs

Uncertainty Wrappers for Data-Driven Models - Increase the Transparency of AI/ML-Based Models Through Enrichment with Dependable Situation-Aware Uncertainty Estimates

Michael Kläs, Lena Sembach, L. Sembach, 2019, SAFECOMP Workshops.

A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components

Michael Kläs, Lisa Jöckel, Michael Kläs, 2020, SAFECOMP Workshops.

Handling Uncertainty in Collaborative Embedded Systems Engineering

Thorsten Weyer, Boris Wirtz, Sebastian Törsleff, 2021 .

Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software

Silverio Martínez-Fernández, Michael Kläs, Lisa Jöckel, 2019, 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C).

Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics

Michael Kläs, Lisa Jöckel, 2019, SAFECOMP.

Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study pdf

Patrik Feth, Daniel Schneider, Andreas Jedlitschka, 2019, ArXiv.