| Principal neighbourhood aggregation for graph nets G Corso, L Cavalleri, D Beaini, P Liň, P Veličković NeurIPS2020, 2020 | 756 | 2020 |
| Rethinking graph transformers with spectral attention D Kreuzer, D Beaini, W Hamilton, V Létourneau, P Tossou Advances in Neural Information Processing Systems 34, 21618-21629, 2021 | 537 | 2021 |
| Recipe for a general, powerful, scalable graph transformer L Rampášek, M Galkin, VP Dwivedi, AT Luu, G Wolf, D Beaini Advances in Neural Information Processing Systems 35, 14501-14515, 2022 | 520 | 2022 |
| 3d infomax improves gnns for molecular property prediction H Stärk, D Beaini, G Corso, P Tossou, C Dallago, S Günnemann, P Liň International Conference on Machine Learning, 20479-20502, 2022 | 231 | 2022 |
| Long range graph benchmark VP Dwivedi, L Rampášek, M Galkin, A Parviz, G Wolf, AT Luu, D Beaini Advances in Neural Information Processing Systems 35, 22326-22340, 2022 | 201 | 2022 |
| Directional graph networks D Beaini, S Passaro, V Létourneau, W Hamilton, G Corso, P Liň International Conference on Machine Learning, 748-758, 2021 | 200 | 2021 |
| Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot C Bousquet-Jette, S Achiche, D Beaini, YSLK Cio, C Leblond-Ménard, ... Engineering Applications of Artificial Intelligence 63, 33-44, 2017 | 46 | 2017 |
| Towards interpretable sparse graph representation learning with laplacian pooling E Noutahi, D Beaini, J Horwood, S Gigučre, P Tossou arXiv preprint arXiv:1905.11577, 2019 | 41 | 2019 |
| Image-based truss recognition for density-based topology optimization approach JF Gamache, A Vadean, É Noirot-Nérin, D Beaini, S Achiche Structural and Multidisciplinary Optimization 58, 2697-2709, 2018 | 26 | 2018 |
| Gps++: An optimised hybrid mpnn/transformer for molecular property prediction D Masters, J Dean, K Klaser, Z Li, S Maddrell-Mander, A Sanders, H Helal, ... arXiv preprint arXiv:2212.02229, 2022 | 25 | 2022 |
| Towards foundational models for molecular learning on large-scale multi-task datasets D Beaini, S Huang, JA Cunha, Z Li, G Moisescu-Pareja, O Dymov, ... arXiv preprint arXiv:2310.04292, 2023 | 18 | 2023 |
| Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology O Kraus, K Kenyon-Dean, S Saberian, M Fallah, P McLean, J Leung, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 9 | 2024 |
| Graph positional and structural encoder S Cantürk, R Liu, O Lapointe-Gagné, V Létourneau, G Wolf, D Beaini, ... arXiv preprint arXiv:2307.07107, 2023 | 9 | 2023 |
| Gps++: Reviving the art of message passing for molecular property prediction D Masters, J Dean, K Klaser, Z Li, S Maddrell-Mander, A Sanders, H Helal, ... arXiv preprint arXiv:2302.02947, 2023 | 7 | 2023 |
| Deep green function convolution for improving saliency in convolutional neural networks D Beaini, S Achiche, A Duperré, M Raison The Visual Computer 37 (2), 227-244, 2021 | 7 | 2021 |
| Novel convolution kernels for computer vision and shape analysis based on electromagnetism D Beaini, S Achiche, YSLK Cio, M Raison arXiv preprint arXiv:1806.07996, 2018 | 6 | 2018 |
| Computing the spatial probability of inclusion inside partial contours for computer vision applications D Beaini, S Achiche, F Nonez, M Raison arXiv preprint arXiv:1806.01339, 2018 | 6 | 2018 |
| Generating QM1B with PySCF A Mathiasen, H Helal, K Klaser, P Balanca, J Dean, C Luschi, D Beaini, ... Advances in Neural Information Processing Systems 36, 55036-55050, 2023 | 5 | 2023 |
| Design guidelines for shoulder design of an anthropomorphic robotic arm M Leroux, S Achiche, D Beaini, M Raison DS 87-4 Proceedings of the 21st International Conference on Engineering …, 2017 | 5 | 2017 |
| On the Scalability of GNNs for Molecular Graphs M Sypetkowski, F Wenkel, F Poursafaei, N Dickson, K Suri, P Fradkin, ... arXiv preprint arXiv:2404.11568, 2024 | 3 | 2024 |
