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pro vyhledávání: '"Lv Xin"'
Autor:
Lv Xin
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
This paper proposes an adaptive learning model for the interrelationship between civic education and college students’ career planning. Secondly, a competency point tracking model is constructed based on a neural network algorithm to explore the re
Externí odkaz:
https://doaj.org/article/a0e1dc8452ba4fe98acdc9f148f65e6b
Autor:
Qiu Jun, Wang Haoyun, Lv Xin, Mao Lipeng, Huang Junyan, Hao Tao, Li Junliang, Qi Shuo, Chen Guodong, Jiang Haiping
Publikováno v:
Open Life Sciences, Vol 18, Iss 1, Pp 589-604 (2023)
The aim of this study is to explore a novel classification and investigate the clinical significance of hepatocellular carcinoma (HCC) cells. We analyzed integrated single-cell RNA sequencing and bulk RNA-seq data obtained from HCC samples. Cell traj
Externí odkaz:
https://doaj.org/article/191d8984bf7a460a9db00132b6931d0f
Autor:
Liu, Jiyuan, Liu, Xinwang, Wang, Siqi, Hu, Xingchen, Liao, Qing, Wan, Xinhang, Zhang, Yi, Lv, Xin, He, Kunlun
Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart from the model training, existing methods requires the collaboration of
Externí odkaz:
http://arxiv.org/abs/2409.04111
Autor:
Zhang, Jiajie, Bai, Yushi, Lv, Xin, Gu, Wanjun, Liu, Danqing, Zou, Minhao, Cao, Shulin, Hou, Lei, Dong, Yuxiao, Feng, Ling, Li, Juanzi
Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns abou
Externí odkaz:
http://arxiv.org/abs/2409.02897
Autor:
Bai, Yushi, Zhang, Jiajie, Lv, Xin, Zheng, Linzhi, Zhu, Siqi, Hou, Lei, Dong, Yuxiao, Tang, Jie, Li, Juanzi
Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation l
Externí odkaz:
http://arxiv.org/abs/2408.07055
Publikováno v:
Zeitschrift für Kristallographie - New Crystal Structures, Vol 236, Iss 4, Pp 721-723 (2021)
C17H16CuN6OS ⋅ 0.5H2O, monoclinic, P21/c (no. 14), a = 10.3523(4) Å, b = 18.2609(9) Å, c = 9.9688(4) Å, β = 103.918(4)°, Z = 4, V = 1829.21(13) Å3, Rgt(F) = 0.0409, wRref(F2) = 0.1099, T = 291(2) K.
Externí odkaz:
https://doaj.org/article/2ae2ce8c420a4f08b4f916c6a2e7ef54
Publikováno v:
Zeitschrift für Kristallographie - New Crystal Structures, Vol 236, Iss 4, Pp 713-715 (2021)
C34H32Cu2N12O2S2, triclinic, P1‾$P‾{1}$ (no. 2), a = 7.7970(7) Å, b = 9.6110(9) Å, c = 12.7629(12) Å, α = 71.544(8)°, β = 79.322(8)°, γ = 83.734(8)°, Z = 1, V = 890.21(14) Å3, Rgt(F) = 0.0634, wRref(F2) = 0.1827, T = 291(2) K.
Externí odkaz:
https://doaj.org/article/e804382cfc3a407195d60223b0f6a226
Autor:
GLM, Team, Zeng, Aohan, Xu, Bin, Wang, Bowen, Zhang, Chenhui, Yin, Da, Zhang, Dan, Rojas, Diego, Feng, Guanyu, Zhao, Hanlin, Lai, Hanyu, Yu, Hao, Wang, Hongning, Sun, Jiadai, Zhang, Jiajie, Cheng, Jiale, Gui, Jiayi, Tang, Jie, Zhang, Jing, Sun, Jingyu, Li, Juanzi, Zhao, Lei, Wu, Lindong, Zhong, Lucen, Liu, Mingdao, Huang, Minlie, Zhang, Peng, Zheng, Qinkai, Lu, Rui, Duan, Shuaiqi, Zhang, Shudan, Cao, Shulin, Yang, Shuxun, Tam, Weng Lam, Zhao, Wenyi, Liu, Xiao, Xia, Xiao, Zhang, Xiaohan, Gu, Xiaotao, Lv, Xin, Liu, Xinghan, Liu, Xinyi, Yang, Xinyue, Song, Xixuan, Zhang, Xunkai, An, Yifan, Xu, Yifan, Niu, Yilin, Yang, Yuantao, Li, Yueyan, Bai, Yushi, Dong, Yuxiao, Qi, Zehan, Wang, Zhaoyu, Yang, Zhen, Du, Zhengxiao, Hou, Zhenyu, Wang, Zihan
We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most capable model
Externí odkaz:
http://arxiv.org/abs/2406.12793
Autor:
Zhu, Qianchao, Duan, Jiangfei, Chen, Chang, Liu, Siran, Li, Xiuhong, Feng, Guanyu, Lv, Xin, Cao, Huanqi, Chuanfu, Xiao, Zhang, Xingcheng, Lin, Dahua, Yang, Chao
Large language models (LLMs) now support extremely long context windows, but the quadratic complexity of vanilla attention results in significantly long Time-to-First-Token (TTFT) latency. Existing approaches to address this complexity require additi
Externí odkaz:
http://arxiv.org/abs/2406.15486
Providing knowledge documents for large language models (LLMs) has emerged as a promising solution to update the static knowledge inherent in their parameters. However, knowledge in the document may conflict with the memory of LLMs due to outdated or
Externí odkaz:
http://arxiv.org/abs/2404.03577