Search results
Results: 5
Number of items: 5
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Zaghen, O., Eijkelboom, F., Pouplin, A., Liu, C., Welling, M., van de Meent, J.-W., & Bekkers, E. J. (2026). Riemannian Variational Flow Matching for Material and Protein Design. Paper presented at 14th International Conference on Learning Representations, Rio de Janeiro, Brazil. https://doi.org/10.48550/arXiv.2502.12981 -
Zaghen, O., Eijkelboom, F., Pouplin, A., & Bekkers, E. J. (2025). Towards Variational Flow Matching on General Geometries. Paper presented at ICLR 2025 Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy, Singapore, Singapore. https://doi.org/10.48550/arXiv.2502.12981
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Eijkelboom, F., Zimmermann, H., Vadgama, S., Bekkers, E. J., Welling, M., Naesseth, C. A., & van de Meent, J.-W. (2025). Controlled Generation with Equivariant Variational Flow Matching. Proceedings of Machine Learning Research, 267, 15066-15078. https://proceedings.mlr.press/v267/eijkelboom25a.html
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Eijkelboom, F., Bartosh, G., Naesseth, C. A., Welling, M., & van de Meent, J.-W. (2025). Variational Flow Matching for Graph Generation. 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. 11735-11764). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-0374
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Liu, C., Ruhe, D., Eijkelboom, F., & Forré, P. (2024). Clifford Group Equivariant Simplicial Message Passing Neural Networks. In The Twelfth International Conference on Learning Representations: ICLR 2024 https://doi.org/10.48550/arXiv.2402.10011
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