| Adversarial examples on graph data: Deep insights into attack and defense H Wu, C Wang, Y Tyshetskiy, A Docherty, K Lu, L Zhu 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019 | 512 | 2019 |
| Hpdedup: A hybrid prioritized data deduplication mechanism for primary storage in the cloud H Wu, C Wang, Y Fu, S Sakr, L Zhu, K Lu 33rd International Symposium on Mass Storage System and Technology (MSST), 2017 | 79 | 2017 |
| A differentiated caching mechanism to enable primary storage deduplication in clouds H Wu, C Wang, Y Fu, S Sakr, K Lu, L Zhu IEEE Transactions on Parallel and Distributed Systems 29 (6), 1202-1216, 2018 | 28 | 2018 |
| Sharing deep neural network models with interpretation H Wu, C Wang, J Yin, K Lu, L Zhu Proceedings of the 2018 World Wide Web Conference (WWW), 177-186, 2018 | 25 | 2018 |
| Towards big data analytics across multiple clusters D Wu, S Sakr, L Zhu, H Wu 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2017 | 13 | 2017 |
| Towards defense against adversarial attacks on graph neural networks via calibrated co-training XG Wu, HJ Wu, X Zhou, X Zhao, K Lu Journal of Computer Science and Technology 37 (5), 1161-1175, 2022 | 9 | 2022 |
| Interpreting shared deep learning models via explicable boundary trees H Wu, C Wang, J Yin, K Lu, L Zhu arXiv preprint arXiv:1709.03730, 2017 | 9 | 2017 |
| One size does not fit all: The case for chunking configuration in backup deduplication H Wu, C Wang, K Lu, Y Fu, L Zhu 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2018 | 6 | 2018 |
| SMINT: Toward interpretable and robust model sharing for deep neural networks H Wu, C Wang, R Nock, W Wang, J Yin, K Lu, L Zhu ACM Transactions on the Web (TWEB) 14 (3), 1-28, 2020 | 5 | 2020 |
| Leveraging free labels to power up heterophilic graph learning in weakly-supervised settings: An empirical study X Wu, H Wu, R Wang, D Li, X Zhou, K Lu European Conference on Machine Learning and Knowledge Discovery in Databases …, 2023 | 4 | 2023 |
| Graphlearner: Graph node clustering with fully learnable augmentation X Yang, E Min, K Liang, Y Liu, S Wang, S Zhou, H Wu, X Liu, E Zhu Proceedings of the 32nd ACM International Conference on Multimedia （ACM MM …, 2024 | 2 | 2024 |
| A case based deep neural network interpretability framework and its user study R Nadeem, H Wu, H Paik, C Wang Web Information Systems Engineering–WISE 2019: 20th International Conference …, 2019 | 2 | 2019 |
| StageFS: A parallel file system optimizing metadata performance for SSD based clusters H Wu, L Zhu, K Lu, G Li, D Wu 2016 IEEE Trustcom/BigDataSE/ISPA, 2147-2152, 2016 | 2 | 2016 |
| Automatic and Aligned Anchor Learning Strategy for Multi-View Clustering H Ma, S Wang, S Yu, S Liu, JJ Huang, H Wu, X Liu, E Zhu Proceedings of the 32nd ACM International Conference on Multimedia （ACM MM）, 2024 | 1 | 2024 |
| Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs D Li, H Wu, M Xie, X Wu, Z Wu, W Zhang 27th European Conference on Artificial Intelligence (ECAI 2024), 2024 | 1* | 2024 |
| Optimizing HPC I/O Performance with Regression Analysis and Ensemble Learning Z Liu, C Zhang, H Wu, J Fang, L Peng, G Ye, Z Tang 2023 IEEE International Conference on Cluster Computing (CLUSTER), 234-246, 2023 | 1 | 2023 |
| Auto-tuning for HPC storage stack: an optimization perspective Z Liu, J Wang, H Wu, Q Ma, L Peng, Z Tang CCF Transactions on High Performance Computing, 1-24, 2024 | | 2024 |
| Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networks K Xia, H Wu, D Li, M Xie, R Wang, W Zhang arXiv preprint arXiv:2410.12425, 2024 | | 2024 |
| Fully Decentralized Data Distribution for Exascale-HPC: End of the Provider-Demander Matching Puzzle M Shao, W Zhang, R Wang, H Wu, Y Dai, K Lu 2024 IEEE International Conference on Cluster Computing (CLUSTER), 310-321, 2024 | | 2024 |
| Towards adaptive graph neural networks via solving prior-data conflicts X Wu, H Wu, R Wang, X Zhou, K Lu Frontiers of Information Technology & Electronic Engineering 25, 369–383, 2024 | | 2024 |