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Results: 11
Number of items: 11
  • Open Access
    Gerdes, M., de Haan, P., Bondesan, R., & Cheng, M. C. N. (2025). Nonperturbative trivializing flows for lattice gauge theories. Physical Review D, 112(9), Article 094516. https://doi.org/10.1103/31d5-hvp6
  • Open Access
    de Haan, P. (2025). Machine learning with generalised symmetries. [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
  • Gerdes, M., de Haan, P., Rainone, C., Bondesan, R., & Cheng, M. C. N. (2023, January 18). Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7547918
  • 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
    Gerdes, M., de Haan, P., Rainone, C., Bondesan, R., & Cheng, M. C. N. (2023). Learning lattice quantum field theories with equivariant continuous flows. SciPost Physics, 15(6), Article 238. https://doi.org/10.21468/SciPostPhys.15.6.238
  • Suk, J., de Haan, P., Lippe, P., Brune, C., & Wolterink, J. M. (2022). Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models. In E. Puyol Antón, M. Pop, C. Martín-Isla, M. Sermesant, A. Suinesiaputra, O. Camara, K. Lekadir, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge: 12th International Workshop, STACOM 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : revised selected papers (pp. 93-102). (Lecture Notes in Computer Science; Vol. 13131). Springer. https://doi.org/10.1007/978-3-030-93722-5_11
  • Open Access
    De Haan, P., Cohen, T. S., & Welling, M. (2021). Natural Graph Networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 5, pp. 3636-3646). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2517756c5a9be6ac007fe9bb7fb92611-Abstract.html
  • Open Access
    de Haan, P., Jayaraman, D., & Levine, S. (2020). Causal Confusion in Imitation Learning. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 15, pp. 11666-11677). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/947018640bf36a2bb609d3557a285329-Abstract.html
  • Open Access
    Falorsi, L., de Haan, P., Davidson, T. R., & Forré, P. (2019). Reparameterizing Distributions on Lie Groups. Proceedings of Machine Learning Research, 89, 3244-3253. https://arxiv.org/abs/1903.02958
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