Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Yuan, Jingyang"'
Autor:
Luo, Junyu, Xiao, Zhiping, Wang, Yifan, Luo, Xiao, Yuan, Jingyang, Ju, Wei, Liu, Langechuan, Zhang, Ming
Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplo
Externí odkaz:
http://arxiv.org/abs/2408.12185
Retrieval-augmented generation (RAG) is a framework enabling large language models (LLMs) to enhance their accuracy and reduce hallucinations by integrating external knowledge bases. In this paper, we introduce a hybrid RAG system enhanced through a
Externí odkaz:
http://arxiv.org/abs/2408.05141
Autor:
Huang, Jinsheng, Chen, Liang, Guo, Taian, Zeng, Fu, Zhao, Yusheng, Wu, Bohan, Yuan, Ye, Zhao, Haozhe, Guo, Zhihui, Zhang, Yichi, Yuan, Jingyang, Ju, Wei, Liu, Luchen, Liu, Tianyu, Chang, Baobao, Zhang, Ming
Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image, a question, and several options. However, many benchmarks used for suc
Externí odkaz:
http://arxiv.org/abs/2407.00468
Autor:
DeepSeek-AI, Liu, Aixin, Feng, Bei, Wang, Bin, Wang, Bingxuan, Liu, Bo, Zhao, Chenggang, Dengr, Chengqi, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Xu, Hanwei, Yang, Hao, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Chen, Jin, Yuan, Jingyang, Qiu, Junjie, Song, Junxiao, Dong, Kai, Gao, Kaige, Guan, Kang, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Pan, Ruizhe, Xu, Runxin, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Zheng, Size, Wang, T., Pei, Tian, Yuan, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Liu, Xin, Xie, Xin, Yu, Xingkai, Song, Xinnan, Zhou, Xinyi, Yang, Xinyu, Lu, Xuan, Su, Xuecheng, Wu, Y., Li, Y. K., Wei, Y. X., Zhu, Y. X., Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Zheng, Yi, Zhang, Yichao, Xiong, Yiliang, Zhao, Yilong, He, Ying, Tang, Ying, Piao, Yishi, Dong, Yixin, Tan, Yixuan, Liu, Yiyuan, Wang, Yongji, Guo, Yongqiang, Zhu, Yuchen, Wang, Yuduan, Zou, Yuheng, Zha, Yukun, Ma, Yunxian, Yan, Yuting, You, Yuxiang, Liu, Yuxuan, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Hao, Zhewen, Shao, Zhihong, Wen, Zhiniu, Xu, Zhipeng, Zhang, Zhongyu, Li, Zhuoshu, Wang, Zihan, Gu, Zihui, Li, Zilin, Xie, Ziwei
We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128
Externí odkaz:
http://arxiv.org/abs/2405.04434
Autor:
Ju, Wei, Zhao, Yusheng, Qin, Yifang, Yi, Siyu, Yuan, Jingyang, Xiao, Zhiping, Luo, Xiao, Yan, Xiting, Zhang, Ming
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous d
Externí odkaz:
http://arxiv.org/abs/2403.01091
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmar
Externí odkaz:
http://arxiv.org/abs/2309.14673
Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniques often pr
Externí odkaz:
http://arxiv.org/abs/2306.08194
Autor:
Ju, Wei, Fang, Zheng, Gu, Yiyang, Liu, Zequn, Long, Qingqing, Qiao, Ziyue, Qin, Yifang, Shen, Jianhao, Sun, Fang, Xiao, Zhiping, Yang, Junwei, Yuan, Jingyang, Zhao, Yusheng, Wang, Yifan, Luo, Xiao, Zhang, Ming
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and d
Externí odkaz:
http://arxiv.org/abs/2304.05055
Autor:
Ju, Wei, Fang, Zheng, Gu, Yiyang, Liu, Zequn, Long, Qingqing, Qiao, Ziyue, Qin, Yifang, Shen, Jianhao, Sun, Fang, Xiao, Zhiping, Yang, Junwei, Yuan, Jingyang, Zhao, Yusheng, Wang, Yifan, Luo, Xiao, Zhang, Ming
Publikováno v:
In Neural Networks May 2024 173
Akademický článek
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