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Results: 34
Number of items: 34
  • 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 (pp. 31780-31810). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.52202/075280-1379
  • Open Access
    Knigge, D. M., Romero, D. W., Gu, A., Gavves, E., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & Sonke, J.-J. (2023). Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. In The Eleventh International Conference on Learning Representations: ICLR 2023 https://openreview.net/forum?id=ZW5aK4yCRqU
  • 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
    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
  • 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
    Romero, D. W., Bruintjes, R.-J., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & van Gemert, J. C. (2022). FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. In The Tenth International Conference on Learning Representations: ICLR 2022 https://openreview.net/forum?id=3jooF27-0Wy
  • Open Access
    Romero, D. W., Kuzina, A., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2022). CKConv: Continuous Kernel Convolution For Sequential Data. In The Tenth International Conference on Learning Representations: ICLR 2022 https://doi.org/10.48550/arXiv.2102.02611
  • Open Access
    Lafarge, M. W., Bekkers, E. J., Pluim, J. P. W., Duits, R., & Veta, M. (2021). Roto-translation equivariant convolutional networks: Application to histopathology image analysis. Medical Image Analysis, 68, Article 101849. https://doi.org/10.1016/j.media.2020.101849
  • 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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