| Self-supervised learning on graphs: Contrastive, generative, or predictive L Wu, H Lin, C Tan, Z Gao, SZ Li IEEE Transactions on Knowledge and Data Engineering 35 (4), 4216-4235, 2021 | 274 | 2021 |
| Moganet: Multi-order gated aggregation network S Li, Z Wang, Z Liu, C Tan, H Lin, D Wu, Z Chen, J Zheng, SZ Li The Twelfth International Conference on Learning Representations, 2023 | 97* | 2023 |
| Conditional Local Convolution for Spatio-temporal Meteorological Forecasting H Lin, Z Gao, Y Xu, L Wu, L Li, SZ Li Proceedings of the AAAI Conference on Artificial Intelligence 36, 2022 | 97 | 2022 |
| Graphmixup: Improving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction L Wu, J Xia, Z Gao, H Lin, C Tan, SZ Li Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 85 | 2022 |
| Diffbp: Generative diffusion of 3d molecules for target protein binding H Lin, Y Huang, O Zhang, S Ma, M Liu, X Li, L Wu, S Ji, T Hou, SZQ Li Chemical Science, 2024 | 75 | 2024 |
| Knowledge distillation improves graph structure augmentation for graph neural networks L Wu, H Lin, Y Huang, SZ Li Advances in Neural Information Processing Systems 35, 11815-11827, 2022 | 54 | 2022 |
| Beyond homophily and homogeneity assumption: Relation-based frequency adaptive graph neural networks L Wu, H Lin, B Hu, C Tan, Z Gao, Z Liu, SZ Li IEEE Transactions on Neural Networks and Learning Systems 35 (6), 8497 - 8509, 2023 | 39* | 2023 |
| Quantifying the knowledge in gnns for reliable distillation into mlps L Wu, H Lin, Y Huang, SZ Li International Conference on Machine Learning, 37571-37581, 2023 | 31 | 2023 |
| Extracting low-/high-frequency knowledge from graph neural networks and injecting it into mlps: An effective gnn-to-mlp distillation framework L Wu, H Lin, Y Huang, T Fan, SZ Li Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10351 …, 2023 | 31 | 2023 |
| Mape-ppi: Towards effective and efficient protein-protein interaction prediction via microenvironment-aware protein embedding L Wu, Y Tian, Y Huang, S Li, H Lin, NV Chawla, SZ Li The Twelfth International Conference on Learning Representations, 2024 | 27 | 2024 |
| Homophily-enhanced self-supervision for graph structure learning: Insights and directions L Wu, H Lin, Z Liu, Z Liu, Y Huang, SZ Li IEEE Transactions on Neural Networks and Learning Systems 35 (9), 12358 - 12372, 2023 | 26 | 2023 |
| Exploring Generative Neural Temporal Point Process H Lin, L Wu, G Zhao, P Liu, SZ Li Transactions on Machine Learning Research, 2022 | 25 | 2022 |
| Functional-group-based diffusion for pocket-specific molecule generation and elaboration H Lin, Y Huang, O Zhang, Y Liu, L Wu, S Li, Z Chen, SZ Li Advances in Neural Information Processing Systems 36, 34603 - 34626, 2024 | 18 | 2024 |
| Gnn cleaner: Label cleaner for graph structured data J Xia, H Lin, Y Xu, C Tan, L Wu, S Li, SZ Li IEEE Transactions on Knowledge and Data Engineering 36 (2), 640-651, 2023 | 17 | 2023 |
| A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation L Wu, H Lin, Z Gao, G Zhao, SZ Li IEEE Transactions on Knowledge and Data Engineering 36 (9), 4375 - 4385, 2024 | 16* | 2024 |
| An Extensive Survey with Empirical Studies on Deep Temporal Point Process H Lin, C Tan, L Wu, Z Liu, Z Gao, SZ Li IEEE Transactions on Knowledge and Data Engineering, 2024 | 13* | 2024 |
| Deep geometry handling and fragment-wise molecular 3d graph generation O Zhang, Y Huang, S Cheng, M Yu, X Zhang, H Lin, Y Zeng, M Wang, ... arXiv preprint arXiv:2404.00014, 2024 | 8 | 2024 |
| Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge Y Huang, O Zhang, L Wu, C Tan, H Lin, Z Gao, S Li, S Li ICML 2024 - International Conference on Machine Learning, 2024 | 7 | 2024 |
| Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning L Wu, Y Tian, H Lin, Y Huang, S Li, NV Chawla, SZ Li ICML 2024 - International Conference on Machine Learning, 2024 | 5 | 2024 |
| PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching H Lin, O Zhang, H Zhao, D Jiang, L Wu, Z Liu, Y Huang, SZ Li ICML 2024 - International Conference on Machine Learning, 2024 | 5 | 2024 |
Lirong WuZhejiang University & Westlake Universitywestlake.edu.cn의 이메일 확인됨