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Results: 61
Number of items: 61
  • Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2026). Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks. (Progress in Data Science; Vol. 1). World Scientific. https://doi.org/10.1142/14143
  • Open Access
    Lang, L., de Mulatier, C., Quax, R., & Forré, P. (2026). Abstract Markov Random Fields. Compositionality, 8, Article 1. https://doi.org/10.46298/compositionality-8-1
  • Open Access
    Lang, L. (2026). Mathematical developments in abstract information theory and safe reward learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Pandeva, T., Jonker, M. J., Hamoen, L., Mooij, J. M., & Forré, P. (2025). Robust Multi-view Co-expression Network Inference. Proceedings of Machine Learning Research, 275, 490-513. https://proceedings.mlr.press/v275/pandeva25a.html
  • Open Access
    Pandeva, T. P. (2025). Machine learning for multi-source data integration. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Federici, M. (2025). Information theory for representation learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Lippert, F. (2025). From weather radars to bird migration fluxes: Process-guided machine learning for spatio-temporal forecasting and inference. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Henten, G. B., Boelrijk, J., Kattenberg, C., Bos, T. S., Ensing, B., Forré, P., & Pirok, B. W. J. (2025). Comparison of optimization algorithms for automated method development of gradient profiles. Journal of Chromatography A, 1742, Article 465626. https://doi.org/10.1016/j.chroma.2024.465626
  • Open Access
    Ruhe, D. J. J. (2025). Structured deep learning with applications in astrophysics. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Zhdanov, M., Ruhe, D., Weiler, M., Lucic, A., Forré, P. D., Brandstetter, J., & Forré, P. (2024). Clifford-steerable convolutional neural networks. Proceedings of Machine Learning Research, 235, 61203-612228. https://proceedings.mlr.press/v235/zhdanov24a.html
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