| Building powerful and equivariant graph neural networks with structural message-passing C Vignac, A Loukas, P Frossard Advances in Neural Information Processing Systems 33, 14143-14155, 2020 | 61 | 2020 |
| Equivariant diffusion for molecule generation in 3d E Hoogeboom, VG Satorras, C Vignac, M Welling International Conference on Machine Learning, 8867-8887, 2022 | 58 | 2022 |
| On the choice of graph neural network architectures C Vignac, G Ortiz-Jiménez, P Frossard ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 10 | 2020 |
| Top-N: Equivariant set and graph generation without exchangeability C Vignac, P Frossard International Conference on Learning Representations (ICLR2022), 2022 | 5 | 2022 |
| Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design I Igashov, H Stärk, C Vignac, VG Satorras, P Frossard, M Welling, ... arXiv preprint arXiv:2210.05274, 2022 | 4 | 2022 |
| DiGress: Discrete Denoising diffusion for graph generation C Vignac, I Krawczuk, A Siraudin, B Wang, V Cevher, P Frossard arXiv preprint arXiv:2209.14734, 2022 | 2 | 2022 |
| Modurec: Recommender systems with feature and time modulation J Maroto, C Vignac, P Frossard ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 1 | 2021 |
| Learning anisotropic filters on product graphs CAY Vignac, P Frossard Proceedings of the ICLR Workshop on Representation Learning on Graphs and …, 2019 | | 2019 |
