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Jahr: 2024
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  1. 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.

  2. 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.

  3. 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.

  4. Hoai-Chau Tran; Ho Minh Duy Nguyen; Duy M. Nguyen; TrungTin Nguyen; Ngan Le; Pengtao Xie; Daniel Sonntag; James Zou; Binh T. Nguyen; Mathias Niepert

    Accelerating Transformers with Spectrum-Preserving Token Merging

    In: The Thirty-Eighth Annual Conference on Neural Information Processing Systems. Neural Information Processing Systems (NeurIPS-2024), December 10-15, Canada, JMLR.org, 2024.

  5. Hoai-Chau Tran; Ho Minh Duy Nguyen; Manh-Duy Nguyen; Ngan Hoang Le; Binh T. Nguyen

    Energy Minimizing-based Token Merging for Accelerating Transformers

    In: International Conference on Learning Representations (ICLR), Practical ML for Low Resource Settings Workshop. International Conference on Learning Representations (ICLR), May 11-12, JMLR.org, 2024.

  6. Ho Minh Duy Nguyen; Nina Lukashina; Tai Nguyen; An T. Le; TrungTin Nguyen; Nhat Ho; Jan Peters; Daniel Sonntag; Viktor Zaverkin; Mathias Niepert

    Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

    In: Proceedings of the 41st International Conference on Machine Learning. International Conference on Machine Learning (ICML), July 21-27, Austria, JMLR.org, 2024.

  7. Mohammad Hossein Rimaz; Christiane Plociennik; Martin Ruskowski

    Semantic Asset Administration Shell for Circular Economy

    Mastersthesis, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), 2024.

  8. Jibinraj Antony; Vinit Hegiste; Ali Nazeri; Hooman Tavakoli Ghinani; Snehal Walunj; Christiane Plociennik; Martin Ruskowski

    Enhancing Object Detection Performance for Small Objects Through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios

    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 90-105, ISBN 978-3-031-57496-2, Springer Nature Switzerland, 2024.

  9. Sofija Engelson; Jan Ehrhardt; Timo Kepp; Joshua Niemeijer; Heinz Handels

    LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas

    In: Steve Pieper; Erik Ziegler; Tawa Idris; Bhanusupriya Somarouthu; Reuben Dorent; Gordon Harris; Ron Kikinis (Hrsg.). Machine Learning for Biomedical Imaging (MELBA), Vol. 2, No. MICCAI 2023 LNQ challenge special issue, Pages 817-833, MELBA Journal, 5/2024.

  10. Jana-Rebecca Rehse; Sander J. J. Leemans; Peter Fettke; Jan Martijn E. M. van der Werf

    On Process Discovery Experimentation: Addressing the Need for Research Methodology in Process Discovery

    In: ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 34, No. 1, Article No.: 3, Pages 1-29, Association for Computing Machinery, New York, 2024.

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