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 | 131 | 2024 |
Sketchml: Accelerating distributed machine learning with data sketches J Jiang, F Fu, T Yang, B Cui Proceedings of the 2018 International Conference on Management of Data, 1269 …, 2018 | 131 | 2018 |
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning F Fu, Y Shao, L Yu, J Jiang, H Xue, Y Tao, B Cui Proceedings of the 2021 International Conference on Management of Data, 563-576, 2021 | 100 | 2021 |
Don’t waste your bits! squeeze activations and gradients for deep neural networks via tinyscript F Fu, Y Hu, Y He, J Jiang, Y Shao, C Zhang, B Cui International Conference on Machine Learning, 3304-3314, 2020 | 77 | 2020 |
Blindfl: Vertical federated machine learning without peeking into your data F Fu, H Xue, Y Cheng, Y Tao, B Cui Proceedings of the 2022 International Conference on Management of Data, 1316 …, 2022 | 52 | 2022 |
Dimboost: Boosting gradient boosting decision tree to higher dimensions J Jiang, B Cui, C Zhang, F Fu Proceedings of the 2018 International Conference on Management of Data, 1363 …, 2018 | 49 | 2018 |
An experimental evaluation of large scale GBDT systems F Fu, J Jiang, Y Shao, B Cui arXiv preprint arXiv:1907.01882, 2019 | 34 | 2019 |
Vf-ps: How to select important participants in vertical federated learning, efficiently and securely? J Jiang, L Burkhalter, F Fu, B Ding, B Du, A Hithnawi, B Li, C Zhang Advances in Neural Information Processing Systems 35, 2088-2101, 2022 | 25 | 2022 |
Towards communication-efficient vertical federated learning training via cache-enabled local updates F Fu, X Miao, J Jiang, H Xue, B Cui arXiv preprint arXiv:2207.14628, 2022 | 25 | 2022 |
SKCompress: compressing sparse and nonuniform gradient in distributed machine learning J Jiang, F Fu, T Yang, Y Shao, B Cui The VLDB Journal 29 (5), 945-972, 2020 | 22 | 2020 |
Angel-ptm: A scalable and economical large-scale pre-training system in tencent X Nie, Y Liu, F Fu, J Xue, D Jiao, X Miao, Y Tao, B Cui arXiv preprint arXiv:2303.02868, 2023 | 13 | 2023 |
Analyzing online transaction networks with network motifs J Jiang, Y Hu, X Li, W Ouyang, Z Wang, F Fu, B Cui Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 12 | 2022 |
Osdp: Optimal sharded data parallel for distributed deep learning Y Jiang, F Fu, X Miao, X Nie, B Cui arXiv preprint arXiv:2209.13258, 2022 | 11 | 2022 |
PCG: a privacy preserving collaborative graph neural network training framework X Miao, W Zhang, Y Jiang, F Fu, Y Shao, L Chen, Y Tao, G Cao, B Cui The VLDB Journal 32 (4), 717-736, 2023 | 9 | 2023 |
Pqcache: Product quantization-based kvcache for long context llm inference H Zhang, X Ji, Y Chen, F Fu, X Miao, X Nie, W Chen, B Cui arXiv preprint arXiv:2407.12820, 2024 | 8 | 2024 |
Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference Z Yu, H Li, F Fu, X Miao, B Cui Proceedings of the AAAI Conference on Artificial Intelligence 38 (15), 16605 …, 2024 | 8 | 2024 |
Improving Automatic Parallel Training via Balanced Memory Workload Optimization Y Wang, Y Jiang, X Miao, F Fu, S Zhu, X Nie, Y Tu, B Cui IEEE Transactions on Knowledge and Data Engineering, 2024 | 8 | 2024 |
Generative and contrastive paradigms are complementary for graph self-supervised learning Y Wang, X Yan, C Hu, Q Xu, C Yang, F Fu, W Zhang, H Wang, B Du, ... 2024 IEEE 40th International Conference on Data Engineering (ICDE), 3364-3378, 2024 | 5 | 2024 |
Kvsagg: Secure aggregation of distributed key-value sets Y Wu, S Dong, Y Zhou, Y Zhao, F Fu, T Yang, C Niu, F Wu, B Cui 2023 IEEE 39th International Conference on Data Engineering (ICDE), 1775-1789, 2023 | 5 | 2023 |
Efficiently Training 7B LLM with 1 Million Sequence Length on 8 GPUs P Zhao, H Zhang, F Fu, X Nie, Q Liu, F Yang, Y Peng, D Jiao, S Li, J Xue, ... arXiv preprint arXiv:2407.12117, 2024 | 3 | 2024 |