no code implementations • 3 Sep 2023 • Zhenheng Tang, Yuxin Wang, Xin He, Longteng Zhang, Xinglin Pan, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Bingsheng He, Xiaowen Chu
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs.
no code implementations • 2 Jun 2023 • Ying Li, Xingwei Wang, Rongfei Zeng, Praveen Kumar Donta, Ilir Murturi, Min Huang, Schahram Dustdar
FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy.
no code implementations • 1 Jun 2022 • Rongfei Zeng, Mai Su, Ruiyun Yu, Xingwei Wang
By analyzing the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is a root cause of VC and IT problems in deep learning model.
no code implementations • 27 Jun 2021 • Rongfei Zeng, Chao Zeng, Xingwei Wang, Bo Li, Xiaowen Chu
Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning.
1 code implementation • 11 Jun 2021 • Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, William K. Cheung, Bo Han
However, in an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images.
no code implementations • 22 Feb 2020 • Rongfei Zeng, Shixun Zhang, Jiaqi Wang, Xiaowen Chu
In MEC, edge nodes would not like to voluntarily participate in learning, and they differ in the provision of multi-dimensional resources, both of which might deteriorate the performance of federated learning.