984 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of StuttgartDE21 papers
- Fraunhofer Institute for Industrial EngineeringDE2 papers
- Fraunhofer Institute of Optronics, System Technologies and Image ExploitationDE2 papers
- Heilbronn UniversityDE2 papers
- Software (Germany)DE2 papers
- Stuttgart Media UniversityDE2 papers
- Carnegie Mellon UniversityUS1 paper
- Delft University of TechnologyNL1 paper
- Deutscher WetterdienstDE1 paper
- Esslingen University of Applied SciencesDE1 paper
- Forschungszentrum JülichDE1 paper
- Friedrich-Alexander-Universität Erlangen-NürnbergDE1 paper
43 papers
ScrewSense: Mapping the Invisible - Anomaly Detection and Error Classification in Screwdriving Processes
Tobias Lettner, Tatyana Ivanovska, Anwar Al Assadi +1
The growing volume of process data generated by screwdriving systems, combined with advancements in computing power, data storage, and algorithms, is facilitating the application o…
Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review
Christopher Braun, Julian Raible, Marco F. Huber
In modern industry, keeping complex systems reliable, safe, and efficient hinges on Prognostics and Health Management (PHM). Machine Learning (ML) has largely driven advancements i…
Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition
Anne Sielemann, Lena Loercher, Max-Lion Schumacher +3
In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analy…
From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI
Helena Monke, Benjamin Sae-Chew, Benjamin Fresz +1
The complexity and opacity of neural networks (NNs) pose significant challenges, particularly in high-stakes fields such as healthcare, finance, and law, where understanding decisi…
Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin +2
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional…
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…
ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market
Vincent Bezold, Lukas Baur, Alexander Sauer
The increasing integration of renewable energy sources has led to greater volatility and unpredictability in electricity generation, posing challenges to grid stability. Ancillary…
Status and Future Prospects of the Standardization Framework Industry 4.0: A European Perspective
Olga Meyer, Marvin Boell, Christoph Legat
The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future…
On the similarity of bandwidth-tuned quantum kernels and classical kernels
Roberto Flórez-Ablan, Marco Roth, Jan Schnabel
Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains unce…
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…
Data Efficient Prediction of excited-state properties using Quantum Neural Networks
Manuel Hagelüken, Marco F. Huber, Marco Roth
Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more re…
Quantum Kernel Methods under Scrutiny: A Benchmarking Study
Jan Schnabel, Marco Roth
Since the entry of kernel theory in the field of quantum machine learning, quantum kernel methods (QKMs) have gained increasing attention with regard to both probing promising appl…
The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis
Benjamin Fresz, Vincent Philipp Göbels, Safa Omri +5
Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this c…
Guiding Video Prediction with Explicit Procedural Knowledge
Patrick Takenaka, Johannes Maucher, Marco F. Huber
We propose a general way to integrate procedural knowledge of a domain into deep learning models. We apply it to the case of video prediction, building on top of object-centric dee…
Classification Metrics for Image Explanations: Towards Building Reliable XAI-Evaluations
Benjamin Fresz, Lena Lörcher, Marco Huber
Decision processes of computer vision models - especially deep neural networks - are opaque in nature, meaning that these decisions cannot be understood by humans. Thus, over the l…
Reinforcement learning-based architecture search for quantum machine learning
Frederic Rapp, David A. Kreplin, Marco F. Huber +1
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influen…
Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review
Harry Robertshaw, Lennart Karstensen, Benjamin Jackson +5
Purpose: Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure…
HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots
Florenz Graf, Jochen Lindermayr, Birgit Graf +2
Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This…
ROBUST: 221 Bugs in the Robot Operating System
Christopher S. Timperley, Gijs van der Hoorn, André Santos +2
As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure…
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law
Benjamin Fresz, Elena Dubovitskaya, Danilo Brajovic +2
This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI). While there is much discussion about the AI Act, for which the trilogue of the E…
PerSival: Neural-network-based visualisation for pervasive continuum-mechanical simulations in musculoskeletal biomechanics
David Rosin, Johannes Kässinger, Xingyao Yu +3
This paper presents a novel neural network architecture for the purpose of pervasive visualisation of a 3D human upper limb musculoskeletal system model. Bringing simulation capabi…
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning
Christian Jauch, Timo Leitritz, Marco F. Huber
Manual assembly workers face increasing complexity in their work. Human-centered assistance systems could help, but object recognition as an enabling technology hinders sophisticat…
Information-based Preprocessing of PLC Data for Automatic Behavior Modeling
Brandon K. Sai, Jonas Gram, Thomas Bauernhansl
Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based…
Automated Machine Learning for Remaining Useful Life Predictions
Marc-André Zöller, Fabian Mauthe, Peter Zeiler +2
Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL pr…