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pro vyhledávání: '"Huber, Marco F"'
Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state counterparts. We
Externí odkaz:
http://arxiv.org/abs/2412.09423
Autor:
Fresz, Benjamin, Göbels, Vincent Philipp, Omri, Safa, Brajovic, Danilo, Aichele, Andreas, Kutz, Janika, Neuhüttler, Jens, Huber, Marco F.
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 context, the black-box nature of machine learning models limits the use
Externí odkaz:
http://arxiv.org/abs/2408.02379
Autor:
Nagel, Tobias, Huber, Marco F.
The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex environments that combine physical
Externí odkaz:
http://arxiv.org/abs/2406.19817
We propose a novel architecture design for video prediction in order to utilize procedural domain knowledge directly as part of the computational graph of data-driven models. On the basis of new challenging scenarios we show that state-of-the-art vid
Externí odkaz:
http://arxiv.org/abs/2407.09537
Publikováno v:
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 1076-1084
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 deep models and show that this leads to a better performance than using d
Externí odkaz:
http://arxiv.org/abs/2406.18220
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 influences core properties of the resulting model, they are often chosen heur
Externí odkaz:
http://arxiv.org/abs/2406.02717
Publikováno v:
2024 IEEE Transactions on Robotics (T-RO)
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 paper presents a human-inspired scene perception model to minimize the
Externí odkaz:
http://arxiv.org/abs/2404.17791
Central to the efficacy of prognostics and health management methods is the acquisition and analysis of degradation data, which encapsulates the evolving health condition of engineering systems over time. Degradation data serves as a rich source of i
Externí odkaz:
http://arxiv.org/abs/2403.13694
Autor:
Alt, Benjamin, Stöckl, Florian, Müller, Silvan, Braun, Christopher, Raible, Julian, Alhasan, Saad, Rettig, Oliver, Ringle, Lukas, Katic, Darko, Jäkel, Rainer, Beetz, Michael, Strand, Marcus, Huber, Marco F.
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation o
Externí odkaz:
http://arxiv.org/abs/2402.16542
In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled with the expanding landscape of available forecasting methods, poses sign
Externí odkaz:
http://arxiv.org/abs/2312.08528