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Results: 34
Number of items: 34
  • Open Access
    García-Castellanos, A., Wessels, D. R., Berg, N. J. V. D., Duits, R., Pelt, D. M., & Bekkers, E. J. (2025). Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, & N. Chen (Eds.), 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025): 2-7 December 2025, San Diego, California, USA and 30 November-5 December 2025, Mexico City, Mexico (pp. 168503-168543). (Advances in Neural Information Processing Systems; Vol. 38). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2505.16035, https://doi.org/10.52202/085713-5076
  • 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
    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
    Kolk, M. Z. H., Ruipérez-Campillo, S., Allaart, C. P., Wilde, A. A. M., Knops, R. E., Narayan, S. M., Tjong, F. V. Y., & DEEP RISK investigators (2024). Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias. Scientific Reports, 14, Article 14889. https://doi.org/10.1038/s41598-024-65357-x
  • 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
    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
    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
  • Kofinas, M., Bekkers, E., Nagaraja, N. S., & Gavves, S. (2023, December 13). Electrostatic field dataset - Latent Field Discovery in Interacting Dynamical Systems with Neural Fields [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10631646
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