40 citations · 41 across the 7 of their papers we have counts for
9 papers
Mitigating Shortcut Learning: Texture-Penalized Prototype Networks
Akshay Anilkumar Girija, Elena Hoemann, Frank Köster +1
Standard Convolutional Neural Networks (CNNs) exhibit severe performance degradation due to a strong inductive texture bias that prioritizes local, high-frequency patterns over glo…
Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems
Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2
Artificial Intelligence (AI) offers significant potential for future aviation systems; however, its integration into safety-critical applications requires compliance with the aviat…
On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks
Johann Maximilian Christensen, Thomas Stefani, Elena Hoemann +2
The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and deployment. Aviation, often regarded as th…
Image Quality Dependent Degradation for AI Systems
Yannick Kees, Elena Hoemann, Frank Köster +1
Perception is one of the primary applications where neural networks outperform conventional algorithms. One example is AI systems for automated driving, which can detect pedestrian…
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2
While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence…
Defining Operational Conditions for Safety-Critical AI-Based Systems from Data
Johann Maximilian Christensen, Elena Hoemann, Frank Köster +1
Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in the real world, defining the und…
Towards solving ontological dissonance using network graphs
Maximilian Staebler, Frank Koester, Christoph Schlueter-Langdon
Data Spaces are an emerging concept for the trusted implementation of data-based applications and business models, offering a high degree of flexibility and sovereignty to all stak…
SoK: Assessing the State of Applied Federated Machine Learning
Tobias Müller, Maximilian Stäbler, Hugo Gascón +2
Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy…
Tensor networks for quantum machine learning
Hans-Martin Rieser, Frank Köster, Arne Peter Raulf
Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emer…