Zhenheng Tang
Cited by
Cited by
Deep learning identifies accurate burst locations in water distribution networks
X Zhou, Z Tang, W Xu, F Meng, X Chu, K Xin, G Fu
Water research 166, 115058, 2019
A distributed synchronous SGD algorithm with global Top-k sparsification for low bandwidth networks
S Shi, Q Wang, K Zhao, Z Tang, Y Wang, X Huang, X Chu
2019 IEEE 39th International Conference on Distributed Computing Systems …, 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
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
The impact of GPU DVFS on the energy and performance of deep learning: An empirical study
Z Tang, Y Wang, Q Wang, X Chu
Proceedings of the Tenth ACM International Conference on Future Energy …, 2019
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
C He, AD Shah, Z Tang, DFAN Sivashunmugam, K Bhogaraju, M Shimpi, ...
International Workshop on Trustable, Verifiable and Auditable Federated …, 2021
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
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
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
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, 2022
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
Deep learning identifies leak in water pipeline system using transient frequency response
Z Liao, H Yan, Z Tang, X Chu, T Tao
Process Safety and Environmental Protection 155, 355-365, 2021
Layer-wise adaptive gradient sparsification for distributed deep learning with convergence guarantees
S Shi, Z Tang, Q Wang, K Zhao, X Chu
24th European Conference on Artificial Intelligence, ECAI 2020, 2019
Benchmarking the performance and power of AI accelerators for AI training
Y Wang, Q Wang, S Shi, X He, Z Tang, K Zhao, X Chu
arXiv preprint arXiv:1909.06842, 2019
Data Resampling for Federated Learning with Non-IID Labels
Z Tang, Z Hu, S Shi, Y Cheung, Y Jin, Z Ren, X Chu
FTL-IJCAI 2021, 2021
Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
X He, S Wang, S Shi, Z Tang, Y Wang, Z Zhao, J Dai, R Ni, X Zhang, X Liu, ...
2019 IEEE International Conference on Big Data (Big Data), 4839-4844, 2019
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
Z Tang, Y Wang, X He, L Zhang, X Pan, Q Wang, R Zeng, K Zhao, S Shi, ...
The 32nd International Joint Conference on Artificial Intelligence …, 2023
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension
X He, J Yao, Y Wang, Z Tang, KC Cheung, S See, B Han, X Chu
AAAI 2023, 2022
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
Z Tang, X Chu, RY Ran, S Lee, S Shi, Y Zhang, Y Wang, AQ Liang, ...
arXiv preprint arXiv:2303.01778, 2023
VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting
Y Tang, P Dong, Z Tang, X Chu, J Liang
CVPR Workshop 2024, 2024
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