Zobrazeno 1 - 10
of 515
pro vyhledávání: '"Chen, Yujun"'
How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review
Autor:
Zhu, Xichou, Liu, Yang, Shen, Zhou, Liu, Yi, Li, Min, Chen, Yujun, John, Benzi, Ma, Zhenzhen, Hu, Tao, Li, Zhi, Yang, Bolong, Wang, Manman, Xie, Zongxing, Liu, Peng, Cai, Dan, Wang, Junhui
The recent advances in large language models (LLMs) have significantly expanded their applications across various fields such as language generation, summarization, and complex question answering. However, their application to privacy compliance and
Externí odkaz:
http://arxiv.org/abs/2409.02375
Autor:
Liu, Yang, Zhu, Xichou, Shen, Zhou, Liu, Yi, Li, Min, Chen, Yujun, John, Benzi, Ma, Zhenzhen, Hu, Tao, Li, Zhi, Xu, Zhiyang, Luo, Wei, Wang, Junhui
Large Language Models (LLMs) have recently displayed their extraordinary capabilities in language understanding. However, how to comprehensively assess the sentiment capabilities of LLMs continues to be a challenge. This paper investigates the abilit
Externí odkaz:
http://arxiv.org/abs/2409.02370
Autor:
Zhang, Xuguang, Zhou, Zixuan, Guo, Yijun, Zhuang, Minxue, Jin, Warren, Shen, Bitao, Chen, Yujun, Huang, Jiahui, Tao, Zihan, Jin, Ming, Chen, Ruixuan, Ge, Zhangfeng, Fang, Zhou, Zhang, Ning, Liu, Yadong, Cai, Pengfei, Hu, Weiwei, Shu, Haowen, Pan, Dong, Bowers, John E., Wang, Xingjun, Chang, Lin
Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy ef
Externí odkaz:
http://arxiv.org/abs/2312.08682
Publikováno v:
CVPR 2024
As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent i
Externí odkaz:
http://arxiv.org/abs/2312.08234
Autor:
Wang, Zhihao, Lin, Zongyu, Liu, Peiqi, ZHeng, Guidong, Wen, Junjie, Chen, Xianxin, Chen, Yujun, Yang, Zhilin
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to
Externí odkaz:
http://arxiv.org/abs/2212.13767
Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the manual design of prompts and thus requires a lot of human efforts. In t
Externí odkaz:
http://arxiv.org/abs/2210.17041
Autor:
Sun, Yingying, Lu, Tianli, Pan, Jieyi, He, Haonan, Xu, Mao, Chen, Yujun, Chen, Yan, Fang, Pengchao, Ye, Xiaoxing, Li, Shuxuan, Hu, Haiyan, Yu, Shihui
Publikováno v:
In Colloids and Surfaces B: Biointerfaces October 2024 242
Autor:
Chen, Junyan, Guan, Bin, Zhuang, Zhongqi, Zheng, Chunzheng, Zhou, Jiefei, Su, Tianxu, Chen, Yujun, Zhu, Chenyu, Hu, Xuehan, Zhao, Sikai, Guo, Jiangfeng, Dang, Hongtao, Zhang, Yaoyao, Yuan, Yuheng, Yi, Chao, Xu, Chengze, Xu, Bingyu, Zeng, Wenbo, Li, Yuan, Shi, Kuangyi, He, Yang, Wei, Zhihao, Huang, Zhen
Publikováno v:
In Fuel 1 September 2024 371 Part B
Publikováno v:
In Computers and Fluids 30 August 2024 281
Publikováno v:
In International Journal of Hydrogen Energy 5 August 2024 77:184-192