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Jahr: 2024
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  1. Jan-Micha Bodensohn; Carsten Binnig

    Rethinking Table Retrieval from Data Lakes

    In: Proceedings of the Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2024, Santiago, Chile, 14 June 2024. International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM), Pages 2:1-2:5, ACM, 2024.

  2. Vassilios Yfantis; Achim Wagner; Martin Ruskowski

    Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control

    In: Results in Control and Optimization (RICO), Vol. 17, Pages 1-19, Elsevier, 12/2024.

  3. Benjamin Blumhofer; Jonas Weigand; Leonhard Kunz; Pascal Rübel; Achim Wagner

    Detection of Rare Fault Cases for Mobile Robot Applications

    In: Achim Wagner; Kosmas Alexopoulos; Sotiris Makris (Hrsg.). Advances in Artificial Intelligence in Manufacturing. European Symposium on Artificial Intelligence in Manufacturing (ESAIM-2023), Cham, Pages 61-70, ISBN 978-3-031-57496-2, Springer Nature Switzerland, 2024.

  4. Jibinraj Antony; Dorotea Jalu¨ić; Simon Bergweiler; Ákos Hajnal; Veronika ´labravec; Márk Emődi; Dejan Strbad; Tatjana Legler; Attila Csaba Marosi

    Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications

    In: Journal of Grid Computing, Vol. 22 (71), Pages 1-19, Springer, 11/2024.

  5. Vinit Hegiste; Tatjana Legler; Martin Ruskowski

    Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing

    In: 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). International Conference on Federated Learning Technologies and Applications (FLTA-2024), September 17-20, Valencia, Spain, Pages 55-62, IEEE, 2024.

  6. Tatjana Legler; Vinit Hegiste; Martin Ruskowski

    Seamless Integration: Sampling Strategies in Federated Learning Systems

    In: 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). International Conference on Federated Learning Technologies and Applications (FLTA-2024), September 17-20, Valencia, Spain, Pages 148-155, IEEE, 2024.

  7. Vinit Hegiste; Snehal Walunj; Jibinraj Antony; Tatjana Legler; Martin Ruskowski

    Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques

    In: 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR). International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR-2024), May 14-15, Muscat, Oman, Pages 1-6, ISBN 979-8-3503-4863-7, IEEE, 2024.

  8. Gregor Duwe; Dominique Mercier; Verena Kauth; Kerstin Moench; Markus Junker; Juan Pablo Vesga; Werner Seiz; Juergen Scheele; Andreas Dengel; Axel Haferkamp; Thomas Höfner

    First preliminary results of artificial intelligence generated treatment recommendations for urothelial cancer based on multidisciplinary cancer conferences from the KITTU project

    In: Annals of Oncology, Vol. 35, No. S2, Pages 1161-1161, ESMO, 9/2024.

  9. Gregor Duwe; Verena Kauth; Kerstin Moench; Dominique Mercier; Markus Junker; Juergen Scheele; Werner Seiz; Oliver Pfante; Juan Pablo Vesga Simmins; Natasja De Bruin; Axel Haferkamp; Andreas Dengel; Thomas Höfner

    KITTU: Artificial intelligence supports multidisciplinary cancer conferences – first steps towards revolutionizing clinical decision making in oncology

    In: Reinhard Büttner (Hrsg.). Oncology Research and Treatment, Vol. 47, No. Supl. 1 - 36. Deutscher Krebskongress - Fortschritt gemeinsam gestalten, Pages 20-20, Karger Publishers, 2/2024.

  10. Ho Minh Duy Nguyen; An T. Le; Trung Q. Nguyen; Nghiem T. Diep; Tai Nguyen; Duy Duong-Tran; Jan Peters; Li Shen; Mathias Niepert; Daniel Sonntag

    Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model

    In: The 16th Asian Conference on Machine Learning. Asian Conference on Machine Learning (ACML-2024), December 5-8, Proceedings of Machine Learning Research, 2024.

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