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Results: 5
Number of items: 5
  • Open Access
    Igashov, I., Stärk, H., Vignac, C., Schneuing, A., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., & Correia, B. (2024). Equivariant 3D-conditional diffusion model for molecular linker design. Nature Machine Intelligence, 6(4), 417–427. https://doi.org/10.1038/s42256-024-00815-9
  • Open Access
    García Satorras, V. (2024). Inductive biases for graph neural networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Arts, M., García Satorras, V., Huang, C.-W., Zügner, D., Federici, M., Clementi, C., Noé, F., Pinsler, R., & van den Berg, R. (2023). Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. Journal of Chemical Theory and Computation, 19(18), 6151-6159. https://doi.org/10.1021/acs.jctc.3c00702
  • Open Access
    Hoogeboom, E., Garcia Satorras, V., Tomczak, J., & Welling, M. (2021). The Convolution Exponential and Generalized Sylvester Flows. 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. 22, pp. 18249-18248). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/d3f06eef2ffac7faadbe3055a70682ac-Abstract.html
  • Open Access
    Garcia Satorras, V., Hoogeboom, E., & Welling, M. (2021). E(n) Equivariant Graph Neural Networks. Proceedings of Machine Learning Research, 139, 9323-9332. https://proceedings.mlr.press/v139/satorras21a.html
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