Zobrazeno 1 - 10
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pro vyhledávání: '"Chen, Zhipeng"'
Autor:
Zhu, Yutao, Zhou, Kun, Mao, Kelong, Chen, Wentong, Sun, Yiding, Chen, Zhipeng, Cao, Qian, Wu, Yihan, Chen, Yushuo, Wang, Feng, Zhang, Lei, Li, Junyi, Wang, Xiaolei, Wang, Lei, Zhang, Beichen, Dong, Zican, Cheng, Xiaoxue, Chen, Yuhan, Tang, Xinyu, Hou, Yupeng, Ren, Qiangqiang, Pang, Xincheng, Xie, Shufang, Zhao, Wayne Xin, Dou, Zhicheng, Mao, Jiaxin, Lin, Yankai, Song, Ruihua, Xu, Jun, Chen, Xu, Yan, Rui, Wei, Zhewei, Hu, Di, Huang, Wenbing, Gao, Ze-Feng, Chen, Yueguo, Lu, Weizheng, Wen, Ji-Rong
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of
Externí odkaz:
http://arxiv.org/abs/2406.19853
Large language models (LLMs) are still struggling in aligning with human preference in complex tasks and scenarios. They are prone to overfit into the unexpected patterns or superficial styles in the training data. We conduct an empirical study that
Externí odkaz:
http://arxiv.org/abs/2406.12606
Autor:
Khassanov, Yerbolat, Chen, Zhipeng, Chen, Tianfeng, Chong, Tze Yuang, Li, Wei, Zhang, Jun, Lu, Lu, Wang, Yuxuan
This paper addresses challenges in integrating new languages into a pre-trained multilingual automatic speech recognition (mASR) system, particularly in scenarios where training data for existing languages is limited or unavailable. The proposed meth
Externí odkaz:
http://arxiv.org/abs/2406.07842
Autor:
Zhou, Kun, Zhang, Beichen, Wang, Jiapeng, Chen, Zhipeng, Zhao, Wayne Xin, Sha, Jing, Sheng, Zhichao, Wang, Shijin, Wen, Ji-Rong
Mathematical reasoning is an important capability of large language models~(LLMs) for real-world applications. To enhance this capability, existing work either collects large-scale math-related texts for pre-training, or relies on stronger LLMs (\eg
Externí odkaz:
http://arxiv.org/abs/2405.14365
Autor:
Ai, Xinkun, Zheng, Wei, Zhang, Ming, Ding, Yonghua, Chen, Dalong, Chen, Zhongyong, Guo, Bihao, Shen, Chengshuo, Wang, Nengchao, Yang, Zhoujun, Chen, Zhipeng, Pan, Yuan, Shen, Biao, Xiao, Binjia
Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven methods, requiring extensive discharge data for training. However, fu
Externí odkaz:
http://arxiv.org/abs/2404.08241
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 5, p e24721 (2021)
BackgroundThough shock wave lithotripsy (SWL) has developed to be one of the most common treatment approaches for nephrolithiasis in recent decades, its treatment planning is often a trial-and-error process based on physicians’ subjective judgement
Externí odkaz:
https://doaj.org/article/ff110f02172a414f9bcf621dcb37e768
Autor:
Chen, Zhipeng, Zhou, Kun, Zhao, Wayne Xin, Wan, Junchen, Zhang, Fuzheng, Zhang, Di, Wen, Ji-Rong
Reinforcement learning (RL) has been widely used in training large language models (LLMs) for preventing unexpected outputs, eg reducing harmfulness and errors. However, existing RL methods mostly adopt the instance-level reward, which is unable to p
Externí odkaz:
http://arxiv.org/abs/2401.06081
Autor:
Shen, Chengshuo, Li, Jianchao, Ding, Yonghua, Dong, Jiaolong, Wang, Nengchao, Han, Dongliang., Mao, Feiyue, Li, Da, Chen, Zhipeng, Yang, Zhoujun, Chen, Zhongyong, Pan, Yuan, Team, J-Text
Measurement of locked mode (LM) is important for the physical research of Magnetohydrodynamic (MHD) instabilities and plasma disruption. The n = 0 pick-up need to be extracted and subtracted to calculate the amplitude and phase of the LM. A new metho
Externí odkaz:
http://arxiv.org/abs/2311.13763
Autor:
Ai, Xinkun, Zheng, Wei, Zhang, Ming, Ding, Yonghua, Chen, Dalong, Chen, Zhongyong, Shen, Chengshuo, Guo, Bihao, Wang, Nengchao, Yang, Zhoujun, Chen, Zhipeng, Pan, Yuan, Shen, Biao, Xiao, Binjia, team, J-TEXT
In the initial stages of operation for future tokamak, facing limited data availability, deploying data-driven disruption predictors requires optimal performance with minimal use of new device data. This paper studies the issue of data utilization in
Externí odkaz:
http://arxiv.org/abs/2311.10368
The two-dimentional (2D) separatrix shaping plays a crucial role in the confinement of the Field Reversed Configuration (FRC), and the magnetic coils serve as an effective means for its control. In this work we develop a method to optimize the locati
Externí odkaz:
http://arxiv.org/abs/2311.03699