| HET: scaling out huge embedding model training via cache-enabled distributed framework X Miao, H Zhang, Y Shi, X Nie, Z Yang, Y Tao, B Cui Proceedings of the VLDB Endowment 15.2 (2021): 312-320., 2021 | 44 | 2021 |
| Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism X Miao, Y Wang, Y Jiang, C Shi, X Nie, H Zhang, B Cui Proceedings of the VLDB Endowment 16.3 (2022): 470–479., 2022 | 30 | 2022 |
| HET-GMP: A graph-based system approach to scaling large embedding model training X Miao, Y Shi, H Zhang, X Zhang, X Nie, Z Yang, B Cui Proceedings of the 2022 International Conference on Management of Data, 470-480, 2022 | 16 | 2022 |
| Hetu: A highly efficient automatic parallel distributed deep learning system X Miao, X Nie, H Zhang, T Zhao, B Cui Science China. Information Sciences 66 (1), 117101, 2023 | 10 | 2023 |
| Model-enhanced Vector Index H Zhang, Y Wang, Q Chen, R Chang, T Zhang, Z Miao, Y Hou, Y Ding, ... Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Retrieval-Augmented Generation for AI-Generated Content: A Survey P Zhao, H Zhang, Q Yu, Z Wang, Y Geng, F Fu, L Yang, W Zhang, B Cui arXiv preprint arXiv:2402.19473, 2024 | 2 | 2024 |
| Experimental analysis of large-scale learnable vector storage compression H Zhang, P Zhao, X Miao, Y Shao, Z Liu, T Yang, B Cui Proceedings of the VLDB Endowment 17.4 (2023): 808–822., 2023 | 1 | 2023 |
| CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models H Zhang, Z Liu, B Chen, Y Zhao, T Zhao, T Yang, B Cui Proceedings of the ACM on Management of Data 2 (1), 1-28, 2024 | | 2024 |
