| 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 | 366 | 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 | 330 | 2018 |
| 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 | 89 | 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 | 83 | 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 | 62 | 2019 |
| Communication-efficient distributed deep learning: A comprehensive survey Z Tang, S Shi, X Chu, W Wang, B Li arXiv preprint arXiv:2003.06307, 2020 | 44 | 2020 |
| 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 | 44 | 2017 |
| 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 | 40 | 2019 |
| Speeding up convolutional neural networks by exploiting the sparsity of rectifier units S Shi, X Chu arXiv preprint arXiv:1704.07724, 2017 | 39 | 2017 |
| 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 | 36 | 2020 |
| Understanding top-k sparsification in distributed deep learning S Shi, X Chu, KC Cheung, S See arXiv preprint arXiv:1911.08772, 2019 | 36 | 2019 |
| 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, 2021 | 31 | 2021 |
| 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 | 31* | 2020 |
| 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 | 31 | 2020 |
| The GPU-based string matching system in advanced AC algorithm J Peng, H Chen, S Shi 2010 10th IEEE International Conference on Computer and Information …, 2010 | 27 | 2010 |
| 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 | 22 | 2020 |
| A DAG Model of Synchronous Stochastic Gradient Descent in Distributed Deep Learning S Shi, Q Wang, X Chu, B Li 2018 IEEE 24rd International Conference on Parallel and Distributed Systems …, 2018 | 21* | 2018 |
| 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 | 18 | 2021 |
| A Quantitative Survey of Communication Optimizations in Distributed Deep Learning S Shi, Z Tang, X Chu, C Liu, W Wang, B Li IEEE Network, 2020 | 15 | 2020 |
| Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans X He, S Wang, X Chu, S Shi, J Tang, X Liu, C Yan, J Zhang, G Ding AAAI 2021, 2021 | 14 | 2021 |
Xiaowen ChuProfessor, HKUST(GZ)Verified email at ust.hk
