Conference in Berlin, March 21-22, 2024
About the Conference
Cyber-physical systems possess the capability to adjust to evolving demands. When coupled with machine learning, various domains like predictive maintenance, self-optimization, and fault diagnosis spring to mind. An essential condition for realizing this potential is the accessibility of machine learning techniques to engineers.
Therefore, the 7th Machine Learning for Cyber Physical Systems – ML4CPS – conferenceoffers researchers and users from various fields an exchange platform. The conference will take place from 21st till 22th of March 2024 at the Fraunhofer Forum in Berlin. Hosts are the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) and the Helmut Schmidt University (HSU).
Papers may cover, but are not limited to the following topics:
• Large Language Models for CPS: Large language models possess the capability to facilitate human-machine interaction. Their capacity to interpret and generate text unlocks novel opportunities for intelligent automation, contextual comprehension, and seamless integration of various data sources, ultimately enhancing the overall performance and functionality of cyber-physical systems.
• Multimodal Learning: These models possess the capability to facilitate the integration of multiple diverse sensors. Multimodal machine learning models can effectively combine sensor measurements, visual data, contextual information, and various other types of inputs. By using these models, cyber-physical systems can enhance their perception, especially in complex real-world scenarios, leading to improved overall performance.
• Robust Machine Learning: Those techniques play a vital role since they reduce effects of noisy or adversarial data. By incorporating robustness measures, machine learning models can demonstrate improved resilience, enhanced generalization capabilities, and reliable performance when being used for cyber-physical systems.
• Integrating domain knowledge in neural networks: This is a crucial aspect for building robust and high-performing neural networks. There are several ways to incorporate prior knowledge into the neural network, like designing the network architecture, incorporating additional data from simulations, or imposing constraints on the loss function. All approaches lead to improved network performance and adaptability in cyber-physical systems.
Fraunhofer Forum Berlin
Anna-Louisa-Karsch-Straße 2, 10178 Berlin
Paper submission: 10th of January 2024
Reviewer Feedback: 1st of February 2024
Camera-Ready Submission: 1st of March 2024
Papers are chosen on a peer-review basis and accepted papers are published with a unique DOI number. Papers with commercial character will not be taken into consideration. Submissions are limited to eight content pages, including all figures and tables; additional pages containing references are allowed.
Please use the following template for your submission:
Paper Submission will be handled via easychair:
All questions related to paper submissions should be emailed to:
Oliver Niggemann, Helmut-Schmidt-University Hamburg
Jürgen Beyerer, Fraunhofer IOSB/Karlsruher Institut für Technologie (KIT)
Alexander Diedrich, Helmut-Schmidt-University Hamburg
Alexander Windmann, Helmut-Schmidt-University Hamburg
Christian Kühnert, Fraunhofer IOSB
International Program Committee:
Volker Lohweg, HS-OWL, DE
Alexander Fay, HSU, DE
Ingo Pill, Silicon Austria Labs, AT
Patrick Rodler, Alpen-Adria-Universität Klagenfurt, AT
Roni Stern, Ben-Gurion Universität, Israel
Alexander Maier, HS Bielefeld
Kaja Balzereit, HS Bielefeld
Idel Montalvo, IngeniousWare GmbH
Martin Wagner, Technologiezentrum Wasser
Letzte Änderung: 5. December 2023