Fabian Jogl
Univ.Ass. Dipl.-Ing. / BSc
Roles
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PreDoc Researcher
Databases and Artificial Intelligence, E192-02 -
PreDoc Researcher
Machine Learning, E194-06
Contact
- fabian.jogl@tuwien.ac.at
- Erzherzog-Johann-Platz 1, Room FB0205
- vCard from TISS
Courses
2023W
- Introduction to Machine Learning / 194.025 / VU
- Machine Learning Algorithms and Applications / 194.101 / PR
- Project in Computer Science 1 / 194.145 / PR
- Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
Publications
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Weisfeiler and Leman Return with Graph Transformations
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Jogl, F., Thiessen, M., & Gärtner, T. (2022). Weisfeiler and Leman Return with Graph Transformations. In 18th International Workshop on Mining and Learning with Graphs - Accepted Papers. 18th International Workshop on Mining and Learning with Graphs, Grenoble, France. https://doi.org/10.34726/3829
Download: Full paper as PDF (439 KB) -
Reducing Learning on Cell Complexes to Graphs
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Jogl, F., Thiessen, M., & Gärtner, T. (2022). Reducing Learning on Cell Complexes to Graphs. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR 2022 Workshop on Geometrical and Topological Representation Learning, International. https://doi.org/10.34726/3421
Download: Paper as PDF (263 KB) -
Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation
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Helm, D., Jogl, F., & Kampel, M. (2022). Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 2087–2091). https://doi.org/10.1109/ICIP46576.2022.9897300
Project: VHH (2019–2023) -
Do we need to Improve message passing? Improving graph neural networks with graph transformations
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Jogl, F. (2022). Do we need to Improve message passing? Improving graph neural networks with graph transformations [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.103141
Download: PDF (897 KB)