| Benchmarking state-of-the-art deep learning software tools S Shi, Q Wang, P Xu, X Chu 2016 7th International Conference on Cloud Computing and Big Data (CCBD), 99-104, 2016 | 472 | 2016 |
| Highly scalable deep learning training system with mixed-precision: Training imagenet in four minutes X Jia, S Song, W He, Y Wang, H Rong, F Zhou, L Xie, Z Guo, Y Yang, L Yu, ... NeurIPS Workshop on Systems for ML and Open Source Software, 2018 | 462 | 2018 |
| A Distributed Synchronous SGD Algorithm with Global Top- Sparsification for Low Bandwidth Networks S Shi, Q Wang, K Zhao, Z Tang, Y Wang, X Huang, X Chu IEEE ICDCS 2019, 2019 | 175 | 2019 |
| Communication-efficient distributed deep learning: A comprehensive survey Z Tang, S Shi, W Wang, B Li, X Chu arXiv preprint arXiv:2003.06307, 2020 | 158 | 2020 |
| Performance modeling and evaluation of distributed deep learning frameworks on gpus S Shi, Q Wang, X Chu 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th …, 2018 | 131 | 2018 |
| Understanding top-k sparsification in distributed deep learning S Shi, X Chu, KC Cheung, S See arXiv preprint arXiv:1911.08772, 2019 | 113 | 2019 |
| MG-WFBP: Efficient data communication for distributed synchronous SGD algorithms S Shi, X Chu, B Li IEEE INFOCOM 2019-IEEE International Conference on Computer Communications …, 2019 | 111 | 2019 |
| Benchmarking the performance and energy efficiency of AI accelerators for AI training Y Wang, Q Wang, S Shi, X He, Z Tang, K Zhao, X Chu 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet …, 2020 | 98* | 2020 |
| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Z Tang, Y Zhang, S Shi, X He, B Han, X Chu ICML 2022, 2022 | 93 | 2022 |
| FADNet: A Fast and Accurate Network for Disparity Estimation Q Wang, S Shi, S Zheng, K Zhao, X Chu International Conference on Robotics and Automation (ICRA) 2020, 2020 | 93 | 2020 |
| GossipFL: A Decentralized Federated Learning Framework with Sparsified and Adaptive Communication Z Tang, S Shi, B Li, X Chu IEEE Transactions on Parallel and Distributed Systems 34 (3), 909 - 922, 2023 | 90 | 2023 |
| A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification S Shi, K Zhao, Q Wang, Z Tang, X Chu IJCAI, 3411-3417, 2019 | 88 | 2019 |
| Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning L Zhang, L Zhang, S Shi, X Chu, B Li arXiv preprint arXiv:2308.03303, 2023 | 84 | 2023 |
| Performance evaluation of deep learning tools in docker containers P Xu, S Shi, X Chu 2017 3rd International Conference on Big Data Computing and Communications …, 2017 | 77 | 2017 |
| Communication-efficient distributed deep learning with merged gradient sparsification on gpus S Shi, Q Wang, X Chu, B Li, Y Qin, R Liu, X Zhao IEEE INFOCOM 2020-IEEE International Conference on Computer Communications, 2020 | 71 | 2020 |
| Communication-efficient decentralized learning with sparsification and adaptive peer selection Z Tang, S Shi, X Chu 2020 IEEE 40th International Conference on Distributed Computing Systems …, 2020 | 69 | 2020 |
| Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters S Shi, X Zhou, S Song, X Wang, Z Zhu, X Huang, X Jiang, F Zhou, Z Guo, ... Fourth Conference on Machine Learning and Systems (MLSys 2021), 2021 | 68 | 2021 |
| Benchmarking deep learning models and automated model design for COVID-19 detection with chest CT scans X He, S Wang, S Shi, X Chu, J Tang, X Liu, C Yan, J Zhang, G Ding MedRxiv, 2020.06. 08.20125963, 2020 | 64 | 2020 |
| Speeding up convolutional neural networks by exploiting the sparsity of rectifier units S Shi, X Chu arXiv preprint arXiv:1704.07724, 2017 | 55 | 2017 |
| A Quantitative Survey of Communication Optimizations in Distributed Deep Learning S Shi, Z Tang, X Chu, C Liu, W Wang, B Li IEEE Network 35 (3), 230 - 237, 2020 | 52 | 2020 |
Xiaowen ChuIEEE Fellow, Professor, Data Science and Analytics, HKUST(GZ)在 ust.hk 的电子邮件经过验证