Zobrazeno 1 - 10
of 70
pro vyhledávání: '"ZHANG Yichang"'
Publikováno v:
You-qi chuyun, Vol 41, Iss 6, Pp 713-720 (2023)
In order to promote the strategic planning for the oil import security of China, relevant influencing factors were studied by integrating document coding with the Decision Making Trial and Evaluation Laboratory-Interpretative Structural Modeling(DEMA
Externí odkaz:
https://doaj.org/article/09518741fe374236a28261bd7f8acb29
Autor:
Yang, An, Yang, Baosong, Hui, Binyuan, Zheng, Bo, Yu, Bowen, Zhou, Chang, Li, Chengpeng, Li, Chengyuan, Liu, Dayiheng, Huang, Fei, Dong, Guanting, Wei, Haoran, Lin, Huan, Tang, Jialong, Wang, Jialin, Yang, Jian, Tu, Jianhong, Zhang, Jianwei, Ma, Jianxin, Yang, Jianxin, Xu, Jin, Zhou, Jingren, Bai, Jinze, He, Jinzheng, Lin, Junyang, Dang, Kai, Lu, Keming, Chen, Keqin, Yang, Kexin, Li, Mei, Xue, Mingfeng, Ni, Na, Zhang, Pei, Wang, Peng, Peng, Ru, Men, Rui, Gao, Ruize, Lin, Runji, Wang, Shijie, Bai, Shuai, Tan, Sinan, Zhu, Tianhang, Li, Tianhao, Liu, Tianyu, Ge, Wenbin, Deng, Xiaodong, Zhou, Xiaohuan, Ren, Xingzhang, Zhang, Xinyu, Wei, Xipin, Ren, Xuancheng, Liu, Xuejing, Fan, Yang, Yao, Yang, Zhang, Yichang, Wan, Yu, Chu, Yunfei, Liu, Yuqiong, Cui, Zeyu, Zhang, Zhenru, Guo, Zhifang, Fan, Zhihao
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to
Externí odkaz:
http://arxiv.org/abs/2407.10671
Large language models (LLMs) have demonstrated impressive capabilities, but still suffer from inconsistency issues (e.g. LLMs can react differently to disturbances like rephrasing or inconsequential order change). In addition to these inconsistencies
Externí odkaz:
http://arxiv.org/abs/2406.12809
Autor:
Bai, Jinze, Bai, Shuai, Chu, Yunfei, Cui, Zeyu, Dang, Kai, Deng, Xiaodong, Fan, Yang, Ge, Wenbin, Han, Yu, Huang, Fei, Hui, Binyuan, Ji, Luo, Li, Mei, Lin, Junyang, Lin, Runji, Liu, Dayiheng, Liu, Gao, Lu, Chengqiang, Lu, Keming, Ma, Jianxin, Men, Rui, Ren, Xingzhang, Ren, Xuancheng, Tan, Chuanqi, Tan, Sinan, Tu, Jianhong, Wang, Peng, Wang, Shijie, Wang, Wei, Wu, Shengguang, Xu, Benfeng, Xu, Jin, Yang, An, Yang, Hao, Yang, Jian, Yang, Shusheng, Yao, Yang, Yu, Bowen, Yuan, Hongyi, Yuan, Zheng, Zhang, Jianwei, Zhang, Xingxuan, Zhang, Yichang, Zhang, Zhenru, Zhou, Chang, Zhou, Jingren, Zhou, Xiaohuan, Zhu, Tianhang
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our la
Externí odkaz:
http://arxiv.org/abs/2309.16609
This paper proposes a new method, OFA-OCR, to transfer multimodal pretrained models to text recognition. Specifically, we recast text recognition as image captioning and directly transfer a unified vision-language pretrained model to the end task. Wi
Externí odkaz:
http://arxiv.org/abs/2212.09297
Autor:
Bai, Jinze, Men, Rui, Yang, Hao, Ren, Xuancheng, Dang, Kai, Zhang, Yichang, Zhou, Xiaohuan, Wang, Peng, Tan, Sinan, Yang, An, Cui, Zeyu, Han, Yu, Bai, Shuai, Ge, Wenbin, Ma, Jianxin, Lin, Junyang, Zhou, Jingren, Zhou, Chang
Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are
Externí odkaz:
http://arxiv.org/abs/2212.04408
The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data
Externí odkaz:
http://arxiv.org/abs/2211.01335
Autor:
Zhao, Xiaojuan, Lin, Shang, Yu, Shuang, Zhang, Yichang, Su, Lin, Geng, Lifang, Cheng, Chenxia, Jiang, Xinqiang
Publikováno v:
In Horticultural Plant Journal May 2024 10(3):853-865
Autor:
Wang, Peng, Lin, Junyang, Yang, An, Zhou, Chang, Zhang, Yichang, Zhou, Jingren, Yang, Hongxia
Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from low coverage and poor faithfulness problems. To mitigate these proble
Externí odkaz:
http://arxiv.org/abs/2105.14778
Autor:
Madsen, Marie Terese Barlebo, Landberg, Rikard, Nielsen, Dennis Sandris, Zhang, Yichang, Anneberg, Olivia Mariella Rosie, Lauritzen, Lotte, Damsgaard, Camilla Trab
Publikováno v:
In The American Journal of Clinical Nutrition January 2024 119(1):18-28