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pro vyhledávání: '"Wang, Zehua"'
Autor:
Wu, Jiaqi, Chen, Simin, Yang, Yuzhe, Li, Yijiang, Hou, Shiyue, Jing, Rui, Wang, Zehua, Chen, Wei, Tian, Zijian
In recent years, large language models (LLMs) have significantly advanced the field of natural language processing (NLP). By fine-tuning LLMs with data from specific scenarios, these foundation models can better adapt to various downstream tasks. How
Externí odkaz:
http://arxiv.org/abs/2411.00985
Federated learning (FL) is a learning paradigm that enables collaborative training of models using decentralized data. Recently, the utilization of pre-trained weight initialization in FL has been demonstrated to effectively improve model performance
Externí odkaz:
http://arxiv.org/abs/2410.23660
Autor:
Wu, Jiaqi, Chen, Simin, Wang, Zehua, Chen, Wei, Tian, Zijian, Yu, F. Richard, Leung, Victor C. M.
As the volume of image data grows, data-oriented cloud computing in Internet of Video Things (IoVT) systems encounters latency issues. Task-oriented edge computing addresses this by shifting data analysis to the edge. However, limited computational p
Externí odkaz:
http://arxiv.org/abs/2411.00838
We consider the problem of model selection in a high-dimensional sparse linear regression model under privacy constraints. We propose a differentially private (DP) best subset selection method with strong statistical utility properties by adopting th
Externí odkaz:
http://arxiv.org/abs/2310.07852
Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face
Externí odkaz:
http://arxiv.org/abs/2307.10507
Autor:
Cai, Yuzhe, Mao, Shaoguang, Wu, Wenshan, Wang, Zehua, Liang, Yaobo, Ge, Tao, Wu, Chenfei, You, Wang, Song, Ting, Xia, Yan, Tien, Jonathan, Duan, Nan, Wei, Furu
Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six
Externí odkaz:
http://arxiv.org/abs/2304.08103
Autor:
Yang, Minghao1,2 (AUTHOR), Wang, Zehua2 (AUTHOR), Yan, Zizhuo2 (AUTHOR), Wang, Wenxiang2 (AUTHOR), Zhu, Qian1 (AUTHOR), Jin, Changlong1 (AUTHOR) cljin@sdu.edu.cn
Publikováno v:
BMC Bioinformatics. 10/14/2024, Vol. 25 Issue 1, p1-13. 13p.
Publikováno v:
Electronic Research Archive. Sep2024, Vol. 32 Issue 9, p1-15. 15p.
Cervical glandular cell (GC) detection is a key step in computer-aided diagnosis for cervical adenocarcinomas screening. It is challenging to accurately recognize GCs in cervical smears in which squamous cells are the major. Widely existing Out-Of-Di
Externí odkaz:
http://arxiv.org/abs/2205.14625
Autor:
Wang, Tixiao, Fan, Yuchen, Tan, Siyu, Wang, Zehua, Li, Mengzhen, Guo, Xiaowei, Yu, Xiangguo, Lin, Qinghai, Song, Xiaojia, Xu, Leiqi, Li, Lixiang, Li, Shiyang, Gao, Lifen, Liang, Xiaohong, Li, Chunyang, Ma, Chunhong
Publikováno v:
In Cell Reports Medicine 19 November 2024 5(11)