Zobrazeno 1 - 10
of 1 764
pro vyhledávání: '"WANG, XIAOQIAN"'
Autor:
Wang, Xiaoqian
Despite the degradability and biocompatibility of poly(α-hydroxy acids), their utility remains limited because their thermal and mechanical properties are inferior to those of commodity polyolefins, which can be attributed to the lack of side-chain
Externí odkaz:
http://hdl.handle.net/10919/113068
Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting in several high-profile lawsuits. The legal landscape is struggling to
Externí odkaz:
http://arxiv.org/abs/2406.12975
Autor:
Zhang, Xuechao, Song, Hongqiang, Zhang, Chengxiao, Fu, Hui, Li, Leping, Li, Jinrong, Wang, Xiaoqian, Wang, Rui, Chen, Yao
The elemental abundance of ICMEs and solar wind near 1 au is often adopted to represent the abundance in the corresponding coronal sources. However, the absolute abundance of heavy ions (relative to hydrogen) near 1 au might be different from the cor
Externí odkaz:
http://arxiv.org/abs/2405.00336
The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern for LLMs, as they may generate factually incorrect responses. In this pa
Externí odkaz:
http://arxiv.org/abs/2404.00942
Autor:
Wu, Xidong, Gao, Shangqian, Zhang, Zeyu, Li, Zhenzhen, Bao, Runxue, Zhang, Yanfu, Wang, Xiaoqian, Huang, Heng
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging. To address the limitation, the Only-Train-Once (OTO) and OTOv2
Externí odkaz:
http://arxiv.org/abs/2403.14729
Alzheimer's disease (AD) is a progressive and irreversible brain disorder that unfolds over the course of 30 years. Therefore, it is critical to capture the disease progression in an early stage such that intervention can be applied before the onset
Externí odkaz:
http://arxiv.org/abs/2403.06087
Autor:
Qin, Hong, Kong, Jude, Ding, Wandi, Ahluwalia, Ramneek, Morr, Christo El, Engin, Zeynep, Effoduh, Jake Okechukwu, Hwa, Rebecca, Guo, Serena Jingchuan, Seyyed-Kalantari, Laleh, Muyingo, Sylvia Kiwuwa, Moore, Candace Makeda, Parikh, Ravi, Schwartz, Reva, Zhu, Dongxiao, Wang, Xiaoqian, Zhang, Yiye
Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustw
Externí odkaz:
http://arxiv.org/abs/2309.05088
Autor:
Chai, Junyi, Wang, Xiaoqian
Adversarial training has been shown to be reliable in improving robustness against adversarial samples. However, the problem of adversarial training in terms of fairness has not yet been properly studied, and the relationship between fairness and acc
Externí odkaz:
http://arxiv.org/abs/2304.00061
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However,
Externí odkaz:
http://arxiv.org/abs/2302.09683
Autor:
Wang, Yu1 (AUTHOR) 2021200025@mails.cust.edu.cn, Wang, Xiaoqian1 (AUTHOR) gaoc@cust.edu.cn, Gao, Chao1 (AUTHOR) yuzhuo@mails.cust.edu.cn, Yu, Zhuo1,2 (AUTHOR) hongwang@mails.cust.edu.cn, Wang, Hong1 (AUTHOR) 2019200015@mails.cust.edu.cn, Zhao, Huan1 (AUTHOR), Yao, Zhihai1 (AUTHOR) xqwang21@cust.edu.cn
Publikováno v:
Sensors (14248220). Jul2024, Vol. 24 Issue 13, p4197. 10p.