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Results: 17
Number of items: 17
  • Open Access
    Lippe, P. (2025). Learning causal representations in spatio-temporal systems. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Suk, J., de Haan, P., Lippe, P., Brune, C., & Wolterink, J. M. (2024). Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall. Computers in Biology and Medicine, 173, Article 108328. https://doi.org/10.1016/j.compbiomed.2024.108328
  • Open Access
    Papa, S., Valperga, R., Knigge, D., Kofinas, M., Lippe, P., Sonke, J.-J., & Gavves, E. (2024). How to Train Neural Field Representations: A Comprehensive Study and Benchmark. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 22616-22625). IEEE Computer Society. https://doi.org/10.48550/arXiv.2312.10531, https://doi.org/10.1109/CVPR52733.2024.02134
  • Papa, S., Valperga, R., Knigge, D., Kofinas, M., Lippe, P., Sonke, J.-J., & Gavves, S. (2023, December 15). Neural Field Arena - Classification [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10392793
  • Lippe, P. (2023, June 15). BISCUIT: Causal Representation Learning from Binary Interactions [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8027138
  • Open Access
    Löwe, S., Lippe, P., Locatello, F., & Welling, M. (2023). Rotating Features for Object Discovery. 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://doi.org/10.48550/arXiv.2306.00600
  • Open Access
    Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). BISCUIT: Causal Representation Learning from Binary Interactions. Proceedings of Machine Learning Research, 216, 1263-1273. https://proceedings.mlr.press/v216/lippe23a.html
  • Open Access
    Brehmer, J., Cohen, T., De Haan, P., & Lippe, P. (2023). Weakly supervised causal representation learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 50, pp. 38319-38331). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2203.16437
  • Open Access
    Lippe, P., Veeling, B. S., Perdikaris, P., Turner, R. E., & Brandstetter, J. (2023). PDE-Refiner: Achieving Accurate Long Rollouts with Temporal Neural PDE Solvers. 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://doi.org/10.48550/arXiv.2308.05732
  • Lippe, P. (2022, June 15). iCITRIS - Causal Representation Learning Datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6570441
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