6 citations · 6 across the 6 of their papers we have counts for
6 papers
LVLM-Aided Alignment of Task-Specific Vision Models
Alexander Koebler, Lukas Kuhn, Ingo Thon +1
In high-stakes domains, small task-specific vision models are crucial due to their low computational requirements and the availability of numerous methods to explain their results.…
Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
Alexander Koebler, Thomas Decker, Ingo Thon +2
We study the problem of monitoring machine learning models under gradual distribution shifts, where circumstances change slowly over time, often leading to unnoticed yet significan…
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
Alexander Koebler, Ingo Thon, Florian Buettner
To ensure the trustworthiness and interpretability of AI systems, it is essential to align machine learning models with human domain knowledge. This can be a challenging and time-c…
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
Alexander Koebler, Ralf Gross, Florian Buettner +1
Flexible industrial production systems will play a central role in the future of manufacturing due to higher product individualization and customization. A key component in such sy…
Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
Thomas Decker, Alexander Koebler, Michael Lebacher +3
Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current…
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
Thomas Decker, Ralf Gross, Alexander Koebler +3
In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current s…