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Results: 23
Number of items: 23
  • Open Access
    Wiers, J., Wessels, D., Alvarez-Florez, L., Bujalance Gomez, A., Ruiperez-Campillo, S., Kolk, M., Bekkers, E., & Tjong, F. (2026). Enhancing stability in cardiac risk stratification with equivariant neural fields. European Heart Journal - Digital Health, 7(Supplement 1), Article ztaf143.052. https://doi.org/10.1093/ehjdh/ztaf143.052
  • Open Access
    Botros, M., Verheijen, L., de Boer, O. J., Halfwerk, H., Brosens, L. A. A., ten Kate, F. J. C., Ooms, A. H. A. G., Oudijk, L., van der Post, C. R. S., van der Wel, M. J., Bekkers, E. J., Kervadec, H., Sánchez, C. I., & Meijer, S. L. (2026). Detecting aberrant p53 immunohistochemical expression patterns in patients with Barrett’s esophagus using artificial intelligence. Journal of Medical Imaging, 13(1), Article 017503. https://doi.org/10.1117/1.JMI.13.1.017503
  • Open Access
    García-Castellanos, A., Medbouhi, A. A., Marchetti, G. L., Bekkers, E. J., & Kragic, D. (2025). HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees. In R. Chowdhury, J. Berry, K. Hanauer, & B. Ren (Eds.), SIAM Symposium on Algorithm Engineering and Experiments (ALENEX25): New Orleans, Louisiana, USA, 12-13 January 2025 (pp. 194-208). Society for Industrial and Applied Mathematics. https://doi.org/10.48550/arXiv.2409.05671, https://doi.org/10.1137/1.9781611978339.16
  • Open Access
    Eijkelboom, F., Zimmermann, H., Vadgama, S., Bekkers, E. J., Welling, M., Naesseth, C. A., & van de Meent, J.-W. (2025). Controlled Generation with Equivariant Variational Flow Matching. Proceedings of Machine Learning Research, 267, 15066-15078. https://proceedings.mlr.press/v267/eijkelboom25a.html
  • Open Access
    Knigge, D. M., Wessels, D. R., Valperga, R., Papa, S., Sonke, J.-J., Gavves, E., & Bekkers, E. J. (2025). Space-Time Continuous PDE Forecasting using Equivariant Neural Fields. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 76553-76577). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-2438
  • Open Access
    Marzella, D. F., Crocioni, G., Radusinović, T., Lepikhov, D., Severin, H., Bodor, D. L., Rademaker, D. T., Lin, C., Georgievska, S., Renaud, N., Kessler, A. L., Lopez-Tarifa, P., Buschow, S. I., Bekkers, E., & Xue, L. C. (2024). Geometric deep learning improves generalizability of MHC-bound peptide predictions. Communications biology, 7, Article 1661. https://doi.org/10.1038/s42003-024-07292-1
  • Open Access
    Islam, M. M., de Vente, C., Liefers, B., Klaver, C., Bekkers, E. J., & Sánchez, C. I. (2024). Uncertainty-aware retinal layer segmentation in OCT through probabilistic signed distance functions. Proceedings of Machine Learning Research, 250, 672-693. https://doi.org/10.48550/arXiv.2412.04935
  • Open Access
    Ranum, O., Wessels, D., Otterspeer, G., Bekkers, E. J., Roelofsen, F., & Andersen, J. I. (2024). The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition. Proceedings of Machine Learning Research, 251, 286-302. https://proceedings.mlr.press/v251/ranum24a.html
  • Open Access
    Moskalev, A. (2024). Representation learning with structured invariance. [Thesis, fully internal, Universiteit van Amsterdam].
  • Kofinas, M., Bekkers, E., Nagaraja, N. S., & Gavves, S. (2023, December 13). Dynamic gravitational field dataset - Latent Field Discovery in Interacting Dynamical Systems with Neural Fields [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10634923
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