| Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods D Lim, F Hohne, X Li, SL Huang, V Gupta, O Bhalerao, SN Lim Advances in neural information processing systems 34, 20887-20902, 2021 | 396 | 2021 |
| Equivariant subgraph aggregation networks B Bevilacqua, F Frasca, D Lim, B Srinivasan, C Cai, G Balamurugan, ... International Conference on Learning Representations, 2022 | 228 | 2022 |
| 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 4, 2022 | 187 | 2022 |
| New Benchmarks for Learning on Non-Homophilous Graphs D Lim, X Li, F Hohne, SN Lim https://arxiv.org/abs/2104.01404, 2021 | 114 | 2021 |
| Graph inductive biases in transformers without message passing L Ma, C Lin, D Lim, A Romero-Soriano, PK Dokania, M Coates, P Torr, ... International Conference on Machine Learning, 23321-23337, 2023 | 112 | 2023 |
| Neural manifold ordinary differential equations A Lou, D Lim, I Katsman, L Huang, Q Jiang, SN Lim, CM De Sa Advances in Neural Information Processing Systems 33, 17548-17558, 2020 | 91 | 2020 |
| Equivariant polynomials for graph neural networks O Puny, D Lim, B Kiani, H Maron, Y Lipman International Conference on Machine Learning, 28191-28222, 2023 | 42 | 2023 |
| The power of recursion in graph neural networks for counting substructures B Tahmasebi, D Lim, S Jegelka International Conference on Artificial Intelligence and Statistics, 11023-11042, 2023 | 41* | 2023 |
| Graph metanetworks for processing diverse neural architectures D Lim, H Maron, MT Law, J Lorraine, J Lucas arXiv preprint arXiv:2312.04501, 2023 | 28 | 2023 |
| Expressive sign equivariant networks for spectral geometric learning D Lim, J Robinson, S Jegelka, H Maron Advances in Neural Information Processing Systems 36, 16426-16455, 2023 | 24 | 2023 |
| Equivariant manifold flows I Katsman, A Lou, D Lim, Q Jiang, SN Lim, CM De Sa Advances in Neural Information Processing Systems 34, 10600-10612, 2021 | 23 | 2021 |
| Doubly Stochastic Subspace Clustering D Lim, R Vidal, B Haeffele arXiv preprint arXiv:2011.14859, 2020 | 22 | 2020 |
| Expertise and dynamics within crowdsourced musical knowledge curation: A case study of the genius platform D Lim, AR Benson Proceedings of the International AAAI Conference on Web and Social Media 15 …, 2021 | 21 | 2021 |
| Structuring representation geometry with rotationally equivariant contrastive learning S Gupta, J Robinson, D Lim, S Villar, S Jegelka arXiv preprint arXiv:2306.13924, 2023 | 18 | 2023 |
| Position: Future directions in the theory of graph machine learning C Morris, F Frasca, N Dym, H Maron, II Ceylan, R Levie, D Lim, ... Forty-first International Conference on Machine Learning, 2024 | 13 | 2024 |
| The doubly stochastic single eigenvalue problem: a computational approach A Harlev, CR Johnson, D Lim Experimental Mathematics 31 (3), 936-945, 2022 | 9 | 2022 |
| Understanding doubly stochastic clustering T Ding, D Lim, R Vidal, BD Haeffele International Conference on Machine Learning, 5153-5165, 2022 | 9 | 2022 |
| A canonization perspective on invariant and equivariant learning G Ma, Y Wang, D Lim, S Jegelka, Y Wang arXiv e-prints, arXiv: 2405.18378, 2024 | 8 | 2024 |
| The empirical impact of neural parameter symmetries, or lack thereof D Lim, T Putterman, R Walters, H Maron, S Jegelka Advances in Neural Information Processing Systems 37, 28322-28358, 2024 | 5 | 2024 |
| Future directions in the theory of graph machine learning C Morris, F Frasca, N Dym, H Maron, İİ Ceylan, R Levie, D Lim, ... arXiv preprint arXiv:2402.02287, 2024 | 4 | 2024 |
Haggai MaronAssistant Professor at the Technion, Research Scientist at NVIDIA ResearchBestätigte E-Mail-Adresse bei technion.ac.il
