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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 Proceedings of the 41st International Conference on Machine Learning. International Conference on Machine Learning (2024), Vienna, Austria. 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)