33 citations · 60 across the 11 of their papers we have counts for
11 papers
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…
In Search of Lost Edges: A Case Study on Reconstructing Financial Networks
Michael Lebacher, Samantha Cook, Nadja Klein +1
To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for…
Tempus Volat, Hora Fugit -- A Survey of Tie-Oriented Dynamic Network Models in Discrete and Continuous Time
Cornelius Fritz, Michael Lebacher, Göran Kauermann
Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-…
Regression-based Network Reconstruction with Nodal and Dyadic Covariates and Random Effects
Michael Lebacher, Göran Kauermann
Network (or matrix) reconstruction is a general problem which occurs if the margins of a matrix are given and the matrix entries need to be predicted. In this paper we show that th…
Censored Regression for Modelling International Small Arms Trading and its "Forensic" Use for Exploring Unreported Trades
Michael Lebacher, Paul W. Thurner, Göran Kauermann
In this paper we use a censored regression model to investigate data on the international trade of small arms and ammunition (SAA) provided by the Norwegian Initiative on Small Arm…
Exploring Dependence Structures in the International Arms Trade Network
Michael Lebacher, Göran Kauermann
In the paper we analyse dependence structures among international trade flows of major conventional weapons from 1952 to 2016. We employ a Network Disturbance Model commonly used i…
A Dynamic Separable Network Model with Actor Heterogeneity: An Application to Global Weapons Transfers
Michael Lebacher, Paul W. Thurner, Göran Kauermann
In this paper we propose to extend the separable temporal exponential random graph model (STERGM) to account for time-varying network- and actor-specific effects. Our application c…