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Akademický článek
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Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Ji, Jiaming, Zhou, Jiayi, Lou, Hantao, Chen, Boyuan, Hong, Donghai, Wang, Xuyao, Chen, Wenqi, Wang, Kaile, Pan, Rui, Li, Jiahao, Wang, Mohan, Dai, Josef, Qiu, Tianyi, Xu, Hua, Li, Dong, Chen, Weipeng, Song, Jun, Zheng, Bo, Yang, Yaodong
Reinforcement learning from human feedback (RLHF) has proven effective in enhancing the instruction-following capabilities of large language models; however, it remains underexplored in the cross-modality domain. As the number of modalities increases
Externí odkaz:
http://arxiv.org/abs/2412.15838
Data-driven deep learning models have enabled tremendous progress in change detection (CD) with the support of pixel-level annotations. However, collecting diverse data and manually annotating them is costly, laborious, and knowledge-intensive. Exist
Externí odkaz:
http://arxiv.org/abs/2412.15541
Autor:
Wu, Jiayi, Cai, Hengyi, Yan, Lingyong, Sun, Hao, Li, Xiang, Wang, Shuaiqiang, Yin, Dawei, Gao, Ming
The emergence of Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in the generation of large language models (LLMs), yet it still reveals numerous limitations. When a general-purpose LLM serves as t
Externí odkaz:
http://arxiv.org/abs/2412.14510
Autor:
PandaX Collaboration, Zhang, Shu, Bo, Zihao, Chen, Wei, Chen, Xun, Chen, Yunhua, Cheng, Zhaokan, Cui, Xiangyi, Fan, Yingjie, Fang, Deqing, Gao, Zhixing, Geng, Lisheng, Giboni, Karl, Guo, Xunan, Guo, Xuyuan, Guo, Zichao, Han, Chencheng, Han, Ke, He, Changda, He, Jinrong, Huang, Di, Huang, Houqi, Huang, Junting, Hou, Ruquan, Hou, Yu, Ji, Xiangdong, Ji, Xiangpan, Ju, Yonglin, Li, Chenxiang, Li, Jiafu, Li, Mingchuan, Li, Shuaijie, Li, Tao, Li, Zhiyuan, Lin, Qing, Liu, Jianglai, Lu, Congcong, Lu, Xiaoying, Luo, Lingyin, Luo, Yunyang, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Ning, Xuyang, Pang, Binyu, Qi, Ningchun, Qian, Zhicheng, Ren, Xiangxiang, Shan, Dong, Shang, Xiaofeng, Shao, Xiyuan, Shen, Guofang, Shen, Manbin, Sun, Wenliang, Tao, Yi, Wang, Anqing, Wang, Guanbo, Wang, Hao, Wang, Jiamin, Wang, Lei, Wang, Meng, Wang, Qiuhong, Wang, Shaobo, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Xu, Wang, Zhou, Wei, Yuehuan, Wu, Weihao, Wu, Yuan, Xiao, Mengjiao, Xiao, Xiang, Xiong, Kaizhi, Xu, Yifan, Yao, Shunyu, Yan, Binbin, Yan, Xiyu, Yang, Yong, Ye, Peihua, Yu, Chunxu, Yuan, Ying, Yuan, Zhe, Yun, Youhui, Zeng, Xinning, Zhang, Minzhen, Zhang, Peng, Zhang, Shibo, Zhang, Tao, Zhang, Wei, Zhang, Yang, Zhang, Yingxin, Zhang, Yuanyuan, Zhao, Li, Zhou, Jifang, Zhou, Jiaxu, Zhou, Jiayi, Zhou, Ning, Zhou, Xiaopeng, Zhou, Yubo, Zhou, Zhizhen
We report the search for neutrinoless double-beta decay of $^{136}$Xe from the PandaX-4T experiment with a 3.7-tonne natural xenon target. The data reconstruction and the background modeling are optimized in the MeV energy region. A blind analysis is
Externí odkaz:
http://arxiv.org/abs/2412.13979
Autor:
Maunu, Tyler, Yao, Jiayi
We develop a new efficient method for high-dimensional sampling called Subspace Langevin Monte Carlo. The primary application of these methods is to efficiently implement Preconditioned Langevin Monte Carlo. To demonstrate the usefulness of this new
Externí odkaz:
http://arxiv.org/abs/2412.13928
Sign Language Production (SLP) aims to generate semantically consistent sign videos from textual statements, where the conversion from textual glosses to sign poses (G2P) is a crucial step. Existing G2P methods typically treat sign poses as discrete
Externí odkaz:
http://arxiv.org/abs/2412.13609
Autor:
Xue, Wangyu, Qian, Chen, Wu, Jiayi, Zhou, Yang, Liu, Wentao, Ren, Ju, Fan, Siming, Zhang, Yaoxue
Existing works on human-centric video understanding typically focus on analyzing specific moment or entire videos. However, many applications require higher precision at the frame level. In this work, we propose a novel task, BestShot, which aims to
Externí odkaz:
http://arxiv.org/abs/2412.12675
Autor:
Jiang, Jinhao, Chen, Jiayi, Li, Junyi, Ren, Ruiyang, Wang, Shijie, Zhao, Wayne Xin, Song, Yang, Zhang, Tao
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge
Externí odkaz:
http://arxiv.org/abs/2412.12881