3 citations · 7 across the 5 of their papers we have counts for
5 papers
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
Yao Wen, Guopeng Zhang, Kezhi Wang +1
To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learni…
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Jialiang Sun, Wen Yao, Tingsong Jiang +1
Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NA…
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Yunyang Zhang, Zhiqiang Gong, Xiaoyu Zhao +1
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering. General reconstruction methods for…
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong +3
Perception of the full state is an essential technology to support the monitoring, analysis, and design of physical systems, one of whose challenges is to recover global field from…
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Yunyang Zhang, Zhiqiang Gong, Weien Zhou +3
Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, larg…