| 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 | 397 | 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 | 77 | 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 | 22 | 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 | 12 | 2017 |
| 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 | 6 | 2017 |
| 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 |
| 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 | 5 | 2018 |
| 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 | 4 | 2022 |
| 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 Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 2 | 2023 |
| 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 | 1 | 2016 |
| 基于内存保护键值的细粒度访存监控. 王睿伯， 吴振伟， 张文喆， 邬会军， 张于， 舒晴， 卢凯 Computer Engineering & Science/Jisuanji Gongcheng yu Kexue 46 (1), 2024 | | 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 | | 2023 |
| Towards adaptive graph neural networks via solving prior-data conflicts X Wu, H Wu, R Wang, X Zhou, K Lu Fronters of Information Technology & Electronic Engineering, 2023 | | 2023 |
| ReForker: Patching x86_64 Binaries with the Fork Server to Improve Hardware-Assisted Fuzzing through Trampoline-Based Binary Rewriting T Yue, P Wang, L Zhou, X Zhou, G Zhang, K Lu, H Wu Proceedings of the 2023 2nd International Conference on Networks …, 2023 | | 2023 |
| Towards integrating learning algorithms into computer system design H Wu UNSW Sydney, 2019 | | 2019 |
