| Point convolutional neural networks by extension operators M Atzmon*, H Maron*, Y Lipman SIGGRAPH 2018, 2018 | 474 | 2018 |
| Provably Powerful Graph Networks H Maron*, H Ben-Hamu*, H Serviansky*, Y Lipman Advances in Neural Information Processing Systems, 2019 | 423 | 2019 |
| Invariant and Equivariant Graph Networks H Maron, H Ben-Hamu, N Shamir, Y Lipman International Conference on Learning Representations (ICLR), 2019 | 352 | 2019 |
| StyleGAN-NADA: CLIP-guided domain adaptation of image generators R Gal, O Patashnik, H Maron, AH Bermano, G Chechik, D Cohen-Or ACM Transactions on Graphics (TOG) 41 (4), 1-13, 2022 | 274 | 2022 |
| Convolutional neural networks on surfaces via seamless toric covers H Maron, M Galun, N Aigerman, M Trope, N Dym, E Yumer, VG Kim, ... ACM Trans. Graph 36 (4), 71, 2017 | 230 | 2017 |
| On the Universality of Invariant Networks H Maron, E Fetaya, N Segol, Y Lipman International Conference on Machine Learning (ICML), 2019 | 196 | 2019 |
| Point registration via efficient convex relaxation H Maron, N Dym, I Kezurer, S Kovalsky, Y Lipman ACM Transactions on Graphics (TOG) 35 (4), 73, 2016 | 138 | 2016 |
| Self-Supervised Learning for Domain Adaptation on Point-Clouds I Achituve, H Maron, G Chechik Winter Conference on Applications of Computer Vision (WACV), 2021, 2021 | 120 | 2021 |
| On Learning Sets of Symmetric Elements H Maron, O Litany, G Chechik, E Fetaya International Conference on Machine Learning (ICML 2020), Outstanding paper …, 2020 | 98 | 2020 |
| Equivariant subgraph aggregation networks B Bevilacqua, F Frasca, D Lim, B Srinivasan, C Cai, G Balamurugan, ... International Conference on Learning Representations (ICLR) 2022 (Spotlight), 2022 | 97 | 2022 |
| Controlling Neural Level Sets M Atzmon, N Haim, L Yariv, O Israelov, H Maron, Y Lipman Advances in Neural Information Processing Systems, 2019 | 93 | 2019 |
| From Local Structures to Size Generalization in Graph Neural Networks G Yehudai, E Fetaya, E Meirom, G Chechik, H Maron International Conference on Machine Learning (ICML) 2021, 2021 | 84 | 2021 |
| DS++: A flexible, scalable and provably tight relaxation for matching problems N Dym*, H Maron*, Y Lipman ACM Transactions on Graphics (TOG) 36 (6), 184:1--184:14, 2017 | 72 | 2017 |
| Multi-chart generative surface modeling H Ben-Hamu, H Maron, I Kezurer, G Avineri, Y Lipman SIGGRAPH Asia 2018 Technical Papers, 215, 2018 | 65 | 2018 |
| On the Universality of Rotation Equivariant Point Cloud Networks N Dym, H Maron International Conference on Learning Representations (ICLR) 2021, 2021 | 57 | 2021 |
| Sign and basis invariant networks for spectral graph representation learning D Lim, J Robinson, L Zhao, T Smidt, S Sra, H Maron, S Jegelka arXiv preprint arXiv:2202.13013, 2022 | 55 | 2022 |
| Weisfeiler and leman go machine learning: The story so far C Morris, Y Lipman, H Maron, B Rieck, NM Kriege, M Grohe, M Fey, ... arXiv preprint arXiv:2112.09992, 2021 | 53 | 2021 |
| Learning Algebraic Multigrid Using Graph Neural Networks I Luz, M Galun, H Maron, R Basri, I Yavneh International Conference on Machine Learning (ICML 2020), 2020 | 51 | 2020 |
| Surface networks via general covers N Haim, N Segol, H Ben-Hamu, H Maron, Y Lipman Proceedings of the IEEE/CVF International Conference on Computer Vision, 632-641, 2019 | 47 | 2019 |
| Understanding and extending subgraph gnns by rethinking their symmetries F Frasca, B Bevilacqua, MM Bronstein, H Maron Advances in Neural Information Processing Systems 2022 (Oral), 2022 | 45 | 2022 |
Yaron LipmanWeizmann Institute, Meta AIVerified email at weizmann.ac.il
