Zobrazeno 1 - 10
of 678
pro vyhledávání: '"Wang, Yaxuan"'
Autor:
Wang, Yaxuan, Wei, Jiaheng, Liu, Chris Yuhao, Pang, Jinlong, Liu, Quan, Shah, Ankit Parag, Bao, Yujia, Liu, Yang, Wei, Wei
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations. Existing approaches to LLM unlearning often rely on retain data or a r
Externí odkaz:
http://arxiv.org/abs/2410.11143
Autor:
Pang, Jinlong, Wei, Jiaheng, Shah, Ankit Parag, Zhu, Zhaowei, Wang, Yaxuan, Qian, Chen, Liu, Yang, Bao, Yujia, Wei, Wei
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. Whi
Externí odkaz:
http://arxiv.org/abs/2410.10877
Autor:
Xing, Tianyi, Liu, Junxuan, Zhang, Likang, Wang, Min-Yan, Li, Yu-Huai, Liu, Ruiyin, Peng, Qingquan, Wang, Dongyang, Wang, Yaxuan, Liu, Hongwei, Li, Wei, Cao, Yuan, Huang, Anqi
One of the most significant vulnerabilities in the source unit of quantum key distribution (QKD) is the correlation between quantum states after modulation, which shall be characterized and evaluated for its practical security performance. In this wo
Externí odkaz:
http://arxiv.org/abs/2408.07960
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ensuring alignment and thus safe use. However, accurately and efficiently
Externí odkaz:
http://arxiv.org/abs/2406.07933
Autor:
Wang, Yizhi, Xue, Shichuan, Wang, Yaxuan, Ding, Jiangfang, Shi, Weixu, Wang, Dongyang, Liu, Yong, Liu, Yingwen, Fu, Xiang, Huang, Guangyao, Huang, Anqi, Deng, Mingtang, Wu, Junjie
Publikováno v:
Optics Letters Vol. 48, Issue 14, pp. 3745-3748 (2023)
Variational quantum algorithms (VQAs) combining the advantages of parameterized quantum circuits and classical optimizers, promise practical quantum applications in the Noisy Intermediate-Scale Quantum era. The performance of VQAs heavily depends on
Externí odkaz:
http://arxiv.org/abs/2310.07371
Autor:
Wang, Yizhi, Xue, Shichuan, Wang, Yaxuan, Liu, Yong, Ding, Jiangfang, Shi, Weixu, Wang, Dongyang, Liu, Yingwen, Fu, Xiang, Huang, Guangyao, Huang, Anqi, Deng, Mingtang, Wu, Junjie
Publikováno v:
Optics Letters Vol. 48, Issue 20, pp. 5197-5200 (2023)
Quantum Generative Adversarial Networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of Noisy Intermediat
Externí odkaz:
http://arxiv.org/abs/2310.00585
Autor:
Wang, Xinfeng1 (AUTHOR) wangxinfeng0211@163.com, Wang, Yaxuan1 (AUTHOR), Yang, Houhong1 (AUTHOR), Liu, Fang1 (AUTHOR), Cai, Yubiao1 (AUTHOR), Xiao, Jing1 (AUTHOR), Fu, Qiang1 (AUTHOR) fuqiang@caas.cn, Wan, Pinjun1 (AUTHOR) fuqiang@caas.cn
Publikováno v:
International Journal of Molecular Sciences. Oct2024, Vol. 25 Issue 20, p10981. 14p.
Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function. We also presented a scalable state repre
Externí odkaz:
http://arxiv.org/abs/2211.10660
Chaotic dynamics of string around the Bardeen-AdS black holes surrounded by quintessence dark energy
We study the motion of a ring string in the background of the Bardeen-AdS black hole surrounded by the quintessence dark energy. The effects of the magnetic monopole charge, the quintessence state parameter, and the quintessence normalization paramet
Externí odkaz:
http://arxiv.org/abs/2210.08641
Autor:
Qi, Junjie, Zhang, Meng, Xu, Ting, Liu, Kun, Wang, Yaxuan, Zhang, Han, Wang, Xuan, Yuan, Zhanhui, Si, Chuanling
Publikováno v:
In Chemical Engineering Journal 15 November 2024 500