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Results: 29
Number of items: 29
  • Open Access
    Zaghen, O., Eijkelboom, F., Pouplin, A., Liu, C., Welling, M., van de Meent, J.-W., & Bekkers, E. J. (2026). Riemannian Variational Flow Matching for Material and Protein Design. Paper presented at 14th International Conference on Learning Representations, Rio de Janeiro, Brazil. https://doi.org/10.48550/arXiv.2502.12981
  • 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
    Liu, R., Lauze, F., Bekkers, E. J., Darkner, S., & Erleben, K. (2025). SE(3) group convolutional neural networks and a study on group convolutions and equivariance for DWI segmentation. Frontiers in Artificial Intelligence, 8, Article 1369717. https://doi.org/10.3389/frai.2025.1369717
  • 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
    Carrasco, M., Zaghen, O., Bekkers, E., & Rieck, B. (2025). Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Them. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2511.03068
  • Open Access
    Zaghen, O., Eijkelboom, F., Pouplin, A., & Bekkers, E. J. (2025). Towards Variational Flow Matching on General Geometries. Paper presented at ICLR 2025 Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy, Singapore, Singapore. https://doi.org/10.48550/arXiv.2502.12981
  • 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
    Islam, M. M., Anand, R., Wessels, D. R., de Kruiff, F., Kuipers, T. P., Ying, R., Sánchez, C. I., Vadgama, S., Bökman, G., & Bekkers, E. J. (2025). Platonic transformers: A solid choice for equivariance. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2510.03511
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