| A better autoencoder for image: Convolutional autoencoder Y Zhang ICONIP17-DCEC. Available online: http://users. cecs. anu. edu. au/Tom …, 2018 | 142 | 2018 |
| COSTA: covariance-preserving feature augmentation for graph contrastive learning Y Zhang, H Zhu, Z Song, P Koniusz, I King Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 133 | 2022 |
| # debatenight: The role and influence of socialbots on twitter during the 1st 2016 us presidential debate MA Rizoiu, T Graham, R Zhang, Y Zhang, R Ackland, L Xie Proceedings of the international AAAI conference on web and social media 12 (1), 2018 | 100 | 2018 |
| Spectral Feature Augmentation for Graph Contrastive Learning and Beyond Y Zhang, H Zhu, Z Song, P Koniusz, I King International Conference on Artificial Intelligence (AAAI), 2023, arXiv …, 2023 | 93 | 2023 |
| A survey of trustworthy federated learning: Issues, solutions, and challenges Y Zhang, D Zeng, J Luo, X Fu, G Chen, Z Xu, I King ACM Transactions on Intelligent Systems and Technology 15 (6), 1-47, 2024 | 54* | 2024 |
| Additively homomorphical encryption based deep neural network for asymmetrically collaborative machine learning Y Zhang, H Zhu IJCAI 20 Federated Learning Workshop; arXiv preprint arXiv:2007.06849, 2020 | 51 | 2020 |
| Towards an optimal asymmetric graph structure for robust semi-supervised node classification Z Song, Y Zhang, I King Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 36 | 2022 |
| Graph-adaptive rectified linear unit for graph neural networks Y Zhang, H Zhu, Z Meng, P Koniusz, I King Proceedings of the ACM Web Conference 2022, https://arxiv.org/abs/2202.06281, 2022 | 34 | 2022 |
| Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space Y Chen, Y Fang, Y Zhang, I King TheWebConf 2023, 2023 | 32 | 2023 |
| Semi-supervised multi-label learning for graph-structured data Z Song, Z Meng, Y Zhang, I King Proceedings of the 30th ACM International Conference on Information …, 2021 | 32 | 2021 |
| WSFE: wasserstein sub-graph feature encoder for effective user segmentation in collaborative filtering Y Chen, Y Zhang, M Yang, Z Song, C Ma, I King Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 25 | 2023 |
| Doc2hash: Learning discrete latent variables for documents retrieval Y Zhang, H Zhu Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 25 | 2019 |
| Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective Y Zhang, H Zhu, Y Chen, Z Song, P Koniusz, I King Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 22 | 2023 |
| Contrastive cross-scale graph knowledge synergy Y Zhang, Y Chen, Z Song, I King Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 22 | 2023 |
| Graph component contrastive learning for concept relatedness estimation Y Ma, Z Song, X Hu, J Li, Y Zhang, I King Proceedings of the AAAI Conference on Artificial Intelligence 37 (11), 13362 …, 2023 | 15 | 2023 |
| Optimal block-wise asymmetric graph construction for graph-based semi-supervised learning Z Song, Y Zhang, I King Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Discrete wasserstein autoencoders for document retrieval Y Zhang, H Zhu ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 12 | 2020 |
| An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching Y Chen, Y Zhang, H Guo, R Tang, I King AACL-IJCNLP, 2022 | 10 | 2022 |
| No change, no gain: empowering graph neural networks with expected model change maximization for active learning Z Song, Y Zhang, I King Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Computational Super-Resolution Imaging With a Sparse Rotational Camera Array Y Zhang, T Li, Y Zhang, P Chen, Y Qu, Z Wei IEEE Transactions on Computational Imaging 9, 425-434, 2023 | 7 | 2023 |
Irwin KingThe Chinese University of Hong Kong在 cse.cuhk.edu.hk 的电子邮件经过验证
