TU Wien Informatics

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Tamara Drucks

Univ.Ass.in Dipl.-Ing.in

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2024W

2025S

 

  • The Expressive Power of Path-Based Graph Neural Networks / Graziani, C., Drucks, T., Jogl, F., Bianchini, M., Scarselli, F., & Gärtner, T. (2024). The Expressive Power of Path-Based Graph Neural Networks. In Z. K. Ruslan Salakhutdinov Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp (Ed.), Proceedings of the 41st International Conference on Machine Learning. PMLR. http://hdl.handle.net/20.500.12708/199519
    Download: PDF (1.68 MB)
    Project: StruDL (2023–2027)
  • Maximally Expressive GNNs for Outerplanar Graphs / Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5433
    Download: PDF (880 KB)
    Project: StruDL (2023–2027)
  • Maximally Expressive GNNs for Outerplanar Graphs / Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023, December 1). Maximally Expressive GNNs for Outerplanar Graphs [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5344
    Downloads: Paper (880 KB) / Poster (422 KB)
    Project: StruDL (2023–2027)
  • ModelRevelator: Fast phylogenetic model estimation via deep learning / Burgstaller-Muehlbacher, S., Crotty, S., Schmidt, H., Reden, F., Drucks, T., & von Haeseler, A. (2023). ModelRevelator: Fast phylogenetic model estimation via deep learning. Molecular Phylogenetics and Evolution, 188, Article 107905. https://doi.org/10.1016/j.ympev.2023.107905
    Download: PDF (5.18 MB)
  • No PAIN no Gain: More Expressive GNNs with Paths / Graziani, C., Drucks, T., Bianchini, M., Scarselli, F., & Gärtner, T. (2023). No PAIN no Gain: More Expressive GNNs with Paths. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5429
    Download: PDF (1.01 MB)
  • Can stochastic weight averaging improve generalization in private learning? / Patrick Indri, Tamara Drucks, & Gärtner, T. (2023). Can stochastic weight averaging improve generalization in private learning? In ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models. ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models, Kigali, Rwanda. https://doi.org/10.34726/5349
    Download: Main paper (366 KB)
  • Representation learning for variable-sized multiple sequence alignments / Drucks, T. (2021). Representation learning for variable-sized multiple sequence alignments [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.88083
    Download: PDF (24.3 MB)
  • On SGD with momentum / Plattner, M. (2023). On SGD with momentum [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.106165
    Download: PDF (1.69 MB)