| CONAN: contrastive fusion networks for multi-view clustering G Ke, Z Hong, Z Zeng, Z Liu, Y Sun, Y Xie 2021 IEEE International Conference on Big Data (Big Data), 653-660, 2021 | 19 | 2021 |
| Efficient multi-view clustering networks G Ke, Z Hong, W Yu, X Zhang, Z Liu Applied Intelligence 52 (13), 14918-14934, 2022 | 11 | 2022 |
| MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion G Ke, Y Zhu, Y Yu International Conference on Data Mining Workshops, (ICDM) 2022, 467--474, 2022 | 5 | 2022 |
| Disentangling multi-view representations beyond inductive bias G Ke, Y Yu, G Chao, X Wang, C Xu, S He Proceedings of the 31st ACM International Conference on Multimedia, 2582-2590, 2023 | 3 | 2023 |
| A Clustering-guided Contrastive Fusion for Multi-view Representation Learning G Ke, G Chao, X Wang, C Xu, Y Zhu, Y Yu IEEE Transactions on Circuits and Systems for Video Technology, 2023 | 3 | 2023 |
| Knowledge distillation-driven semi-supervised multi-view classification X Wang, Y Wang, G Ke, Y Wang, X Hong Information Fusion 103, 102098, 2024 | 2 | 2024 |
| Rethinking Multi-view Representation Learning via Distilled Disentangling G Ke, B Wang, X Wang, S He arXiv preprint arXiv:2403.10897, 2024 | | 2024 |
Shengfeng HeSingapore Management UniversityVerified email at smu.edu.sg
