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
10 papers · 1 filter
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…