| Ps2: Parameter server on spark Z Zhang, B Cui, Y Shao, L Yu, J Jiang, X Miao Proceedings of the 2019 International Conference on Management of Data, 376-388, 2019 | 23 | 2019 |
| Reliable data distillation on graph convolutional network W Zhang, X Miao, Y Shao, J Jiang, L Chen, O Ruas, B Cui Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 20 | 2020 |
| Degnn: Improving graph neural networks with graph decomposition X Miao, NM Gürel, W Zhang, Z Han, B Li, W Min, SX Rao, H Ren, Y Shan, ... Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 15* | 2021 |
| PSGraph: How Tencent trains extremely large-scale graphs with Spark? J Jiang, P Xiao, L Yu, X Li, J Cheng, X Miao, Z Zhang, B Cui 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1549-1557, 2020 | 11 | 2020 |
| Memory-aware framework for efficient second-order random walk on large graphs Y Shao, S Huang, X Miao, B Cui, L Chen Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 9 | 2020 |
| ROD: reception-aware online distillation for sparse graphs W Zhang, Y Jiang, Y Li, Z Sheng, Y Shen, X Miao, L Wang, Z Yang, B Cui Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 8 | 2021 |
| Lasagne: A multi-layer graph convolutional network framework via node-aware deep architecture X Miao, W Zhang, Y Shao, B Cui, L Chen, C Zhang, J Jiang IEEE Transactions on Knowledge and Data Engineering, 2021 | 8 | 2021 |
| Cuwide: Towards efficient flow-based training for sparse wide models on gpus X Miao, L Ma, Z Yang, Y Shao, B Cui, L Yu, J Jiang IEEE Transactions on Knowledge and Data Engineering, 2020 | 6* | 2020 |
| Pointclip: Point cloud understanding by clip R Zhang, Z Guo, W Zhang, K Li, X Miao, B Cui, Y Qiao, P Gao, H Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 5 | 2022 |
| Dense-to-sparse gate for mixture-of-experts X Nie, S Cao, X Miao, L Ma, J Xue, Y Miao, Z Yang, Z Yang, B Cui arXiv preprint arXiv:2112.14397, 2021 | 4 | 2021 |
| 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 arXiv preprint arXiv:2112.07221, 2021 | 4 | 2021 |
| Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce X Miao, X Nie, Y Shao, Z Yang, J Jiang, L Ma, B Cui Proceedings of the 2021 International Conference on Management of Data, 2262 …, 2021 | 4 | 2021 |
| ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest Y Jiang, Y Cheng, H Zhao, W Zhang, X Miao, Y He, L Wang, Z Yang, ... arXiv preprint arXiv:2203.12596, 2022 | 2 | 2022 |
| Road network generation from low frequency GPS trajectory data X Miao, Q Wang, T Zhang 2016 Chinese Control and Decision Conference (CCDC), 6254-6258, 2016 | 2 | 2016 |
| Memory-aware framework for fast and scalable second-order random walk over billion-edge natural graphs Y Shao, S Huang, Y Li, X Miao, B Cui, L Chen The VLDB Journal 30 (5), 769-797, 2021 | 1 | 2021 |
| 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 | | 2022 |
| HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System X Nie, P Zhao, X Miao, B Cui arXiv preprint arXiv:2203.14685, 2022 | | 2022 |
