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Results: 5
Number of items: 5
  • 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
    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
    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
  • 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
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