106 citations
- German Research Centre for Artificial IntelligenceDE7 papers
- University of KaiserslauternDE6 papers
- Technische Universität BerlinDE4 papers
- Max Planck Institute for Intelligent SystemsDE3 papers
- RWTH Aachen UniversityDE2 papers
- Durham UniversityGB1 paper
- Fraunhofer Institute for Industrial EngineeringDE1 paper
- Fraunhofer Institute for Manufacturing Engineering and AutomationDE1 paper
- German University in CairoEG1 paper
- Institut für NanophotonikDE1 paper
- Johannes Kepler University of LinzAT1 paper
- Karlsruhe University of EducationDE1 paper
21 papers
From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles
Omar Y. Goba, Ahmed Y. Gado, Catherine M. Elias +1
Autonomous vehicles (AVs) require adaptive behavior planners to navigate unpredictable, real-world environments safely. Traditional behavior trees (BTs) offer structured decision l…
AutoQML: A Framework for Automated Quantum Machine Learning
Marco Roth, David A. Kreplin, Daniel Basilewitsch +7
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction…
Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing
Max Winkelmann, Constantin Vasconi, Steffen Müller
Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii. To describe scenarios uniformly independe…
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction
Amin E. Bakhshipour, Alireza Koochali, Ulrich Dittmer +3
Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obs…
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain
Alireza Koochali, Maria Walch, Sankrutyayan Thota +3
Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care…
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
Jonas Köhne, Lars Henning, Clemens Gühmann
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). Th…