| Towards deeper graph neural networks with differentiable group normalization K Zhou, X Huang, Y Li, D Zha, R Chen, X Hu Advances in neural information processing systems 33, 4917-4928, 2020 | 218 | 2020 |
| Auto-gnn: Neural architecture search of graph neural networks K Zhou, X Huang, Q Song, R Chen, X Hu Frontiers in big Data 5, 1029307, 2022 | 177 | 2022 |
| Experience replay optimization D Zha, KH Lai, K Zhou, X Hu IJCAI, 2019 | 138 | 2019 |
| Gppt: Graph pre-training and prompt tuning to generalize graph neural networks M Sun, K Zhou, X He, Y Wang, X Wang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 122 | 2022 |
| Dirichlet energy constrained learning for deep graph neural networks K Zhou, X Huang, D Zha, R Chen, L Li, SH Choi, X Hu Advances in Neural Information Processing Systems 34, 21834-21846, 2021 | 110 | 2021 |
| Policy-gnn: Aggregation optimization for graph neural networks KH Lai, D Zha, K Zhou, X Hu Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 102 | 2020 |
| Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study T Chen, K Zhou, K Duan, W Zheng, P Wang, X Hu, Z Wang IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 2769-2781, 2022 | 69 | 2022 |
| EXACT: Scalable graph neural networks training via extreme activation compression Z Liu, K Zhou, F Yang, L Li, R Chen, X Hu International Conference on Learning Representations, 2021 | 58 | 2021 |
| Temporal augmented graph neural networks for session-based recommendations H Zhou, Q Tan, X Huang, K Zhou, X Wang Proceedings of the 44th International ACM SIGIR conference on research and …, 2021 | 57 | 2021 |
| A comprehensive study on large-scale graph training: Benchmarking and rethinking K Duan, Z Liu, P Wang, W Zheng, K Zhou, T Chen, X Hu, Z Wang Advances in Neural Information Processing Systems 35, 5376-5389, 2022 | 55 | 2022 |
| Multi-channel graph neural networks K Zhou, Q Song, X Huang, D Zha, N Zou, X Hu International Joint Conferences on Artificial Intelligence, 2020 | 49* | 2020 |
| Orthogonal graph neural networks K Guo, K Zhou, X Hu, Y Li, Y Chang, X Wang Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 3996-4004, 2022 | 46 | 2022 |
| An information fusion approach to learning with instance-dependent label noise Z Jiang, K Zhou, Z Liu, L Li, R Chen, SH Choi, X Hu International Conference on Learning Representations, 2021 | 38 | 2021 |
| Distributed channel allocation and rate control for hybrid FSO/RF vehicular ad hoc networks K Zhou, C Gong, N Wu, Z Xu Journal of Optical Communications and Networking 9 (8), 669-681, 2017 | 33 | 2017 |
| Autood: Neural architecture search for outlier detection Y Li, Z Chen, D Zha, K Zhou, H Jin, H Chen, X Hu 2021 IEEE 37th International Conference on Data Engineering (ICDE), 2117-2122, 2021 | 30 | 2021 |
| Towards similarity-aware time-series classification D Zha, KH Lai, K Zhou, X Hu Proceedings of the 2022 SIAM International Conference on Data Mining (SDM …, 2022 | 29 | 2022 |
| Color planning and intercell interference coordination for multicolor visible light communication networks K Zhou, C Gong, Z Xu Journal of Lightwave Technology 35 (22), 4980-4993, 2017 | 29 | 2017 |
| Automated anomaly detection via curiosity-guided search and self-imitation learning Y Li, Z Chen, D Zha, K Zhou, H Jin, H Chen, X Hu IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2365-2377, 2021 | 27 | 2021 |
| Autood: Automated outlier detection via curiosity-guided search and self-imitation learning Y Li, Z Chen, D Zha, K Zhou, H Jin, H Chen, X Hu arXiv preprint arXiv:2006.11321, 2020 | 26 | 2020 |
| Compress, then prompt: Improving accuracy-efficiency trade-off of llm inference with transferable prompt Z Xu, Z Liu, B Chen, Y Tang, J Wang, K Zhou, X Hu, A Shrivastava arXiv preprint arXiv:2305.11186, 2023 | 25 | 2023 |
Xia "Ben" HuAssociate Professor of Computer Science, Rice UniversityVerified email at rice.edu
