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Results: 26
Number of items: 26
  • 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
    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
  • Duits, R., Smets, B. M. N., Wemmenhove, A. J., Portegies, J. W., & Bekkers, E. J. (2023). Recent Geometric Flows in Multi-orientation Image Processing via a Cartan Connection. In K. Chen, C.-B. Schönlieb, X.-C. Tai, & L. Younces (Eds.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision (pp. 1525–1583). Springer. https://doi.org/10.1007/978-3-030-98661-2_101, https://doi.org/10.1007/978-3-030-03009-4_101-1
  • Open Access
    Kofinas, M., Bekkers, E. J., Nagaraja, N. S., & Gavves, E. (2023). Latent Field Discovery in Interacting Dynamical Systems with Neural Fields. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/6521bd47ebaa28228cd6c74cb85afb65-Abstract-Conference.html
  • Open Access
    Knigge, D. M., Romero, D. W., & Bekkers, E. J. (2022). Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups. Proceedings of Machine Learning Research, 162, 11359-11386. https://proceedings.mlr.press/v162/knigge22a.html
  • Open Access
    Liu, R., Lauze, F., Bekkers, E., Erleben, K., & Darkner, S. (2022). Group Convolutional Neural Networks for DWI Segmentation. Proceedings of Machine Learning Research, 194, 96-106. https://proceedings.mlr.press/v194/liu22a.html
  • Duits, R., Smets, B., Bekkers, E., & Portegies, J. (2021). Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations. In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, & L. Simon (Eds.), Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021 : proceedings (pp. 27-39). (Lecture Notes in Computer Science; Vol. 12679). Springer. https://doi.org/10.1007/978-3-030-75549-2_3
  • Duits, R., Smets, B. M. N., Wemmenhove, A. J., Portegies, J. W., & Bekkers, E. J. (2021). Recent Geometric Flows in Multi-orientation Image Processing via a Cartan Connection. In K. Chen, C.-B. Schönlieb, X.-C. Tai, & L. Younces (Eds.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision (Living ed.). Springer. https://doi.org/10.1007/978-3-030-03009-4_101-1, https://doi.org/10.1007/978-3-030-98661-2_101
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