8 citations · 22 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning
Thomas Decker, Michael Lebacher, Volker Tresp
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases. However, the opaque nature of many well-performing methods…
Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts
Thomas Decker, Michael Lebacher, Volker Tresp
Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictio…
Towards Scenario-based Safety Validation for Autonomous Trains with Deep Generative Models
Thomas Decker, Ananta R. Bhattarai, Michael Lebacher
Modern AI techniques open up ever-increasing possibilities for autonomous vehicles, but how to appropriately verify the reliability of such systems remains unclear. A common approa…
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…