| Deeper insights into graph convolutional networks for semi-supervised learning Q Li, Z Han, XM Wu Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 3332 | 2018 |
| Attributed graph clustering via adaptive graph convolution X Zhang, H Liu, Q Li, XM Wu arXiv preprint arXiv:1906.01210, 2019 | 339 | 2019 |
| Label efficient semi-supervised learning via graph filtering Q Li, XM Wu, H Liu, X Zhang, Z Guan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 226 | 2019 |
| Unknown intent detection using Gaussian mixture model with an application to zero-shot intent classification G Yan, L Fan, Q Li, H Liu, X Zhang, XM Wu, AYS Lam Proceedings of the 58th annual meeting of the association for computational …, 2020 | 91 | 2020 |
| Large margin meta-learning for few-shot classification Y Wang, XM Wu, Q Li, J Gu, W Xiang, L Zhang, VOK Li Workshop on Meta-Learning (MetaLearn 2018)@ NeurIPS 2018, 2018 | 50* | 2018 |
| Reconstructing capsule networks for zero-shot intent classification H Liu, X Zhang, L Fan, X Fu, Q Li, XM Wu, AYS Lam Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 49 | 2019 |
| Using human feedback to fine-tune diffusion models without any reward model K Yang, J Tao, J Lyu, C Ge, J Chen, W Shen, X Zhu, X Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 43 | 2024 |
| A closer look at the training strategy for modern meta-learning J Chen, XM Wu, Y Li, Q Li, LM Zhan, F Chung Advances in neural information processing systems 33, 396-406, 2020 | 43 | 2020 |
| Recon: Reducing conflicting gradients from the root for multi-task learning G Shi, Q Li, W Zhang, J Chen, XM Wu arXiv preprint arXiv:2302.11289, 2023 | 42 | 2023 |
| Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks Q Dai, XM Wu, L Fan, Q Li, H Liu, X Zhang, D Wang, G Lin, K Yang Pattern Recognition 128, 108628, 2022 | 35 | 2022 |
| Modeling user behavior with graph convolution for personalized product search L Fan, Q Li, B Liu, XM Wu, X Zhang, F Lv, G Lin, S Li, T Jin, K Yang Proceedings of the ACM Web Conference 2022, 203-212, 2022 | 20 | 2022 |
| Dimensionwise separable 2-D graph convolution for unsupervised and semi-supervised learning on graphs Q Li, X Zhang, H Liu, Q Dai, XM Wu Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 20 | 2021 |
| RPC: representative possible world based consistent clustering algorithm for uncertain data H Liu, X Zhang, X Zhang, Q Li, XM Wu Computer Communications 176, 128-137, 2021 | 10* | 2021 |
| Adaptive Graph Convolution Methods for Attributed Graph Clustering X Zhang, H Liu, Q Li, XM Wu, X Zhang IEEE Transactions on Knowledge and Data Engineering 35 (12), 12384-12399, 2023 | 8 | 2023 |
| Neural MMO 2.0: a massively multi-task addition to massively multi-agent learning J Suárez, D Bloomin, KW Choe, HX Li, R Sullivan, N Kanna, D Scott, ... Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Multi-agent path finding via tree lstm Y Jiang, K Zhang, Q Li, J Chen, X Zhu arXiv preprint arXiv:2210.12933, 2022 | 3 | 2022 |
| Boosting decision-based black-box adversarial attack with gradient priors H Liu, X Huang, X Zhang, Q Li, F Ma, W Wang, H Chen, H Yu, X Zhang arXiv preprint arXiv:2310.19038, 2023 | 1 | 2023 |
| Learning on graphs with graph convolution Q Li Hong Kong Polytechnic University, 2023 | 1 | 2023 |
| Simple yet Effective Gradient-Free Graph Convolutional Networks Y Zhu, X Ai, Q Li, XM Wu, K Zhou arXiv preprint arXiv:2302.00371, 2023 | | 2023 |
Xiao-Ming WuAssociate Professor, The Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk