Zobrazeno 1 - 10
of 558
pro vyhledávání: '"Chen, Zhengyu"'
Autor:
Xie, Qianqian, Li, Dong, Xiao, Mengxi, Jiang, Zihao, Xiang, Ruoyu, Zhang, Xiao, Chen, Zhengyu, He, Yueru, Han, Weiguang, Yang, Yuzhe, Chen, Shunian, Zhang, Yifei, Shen, Lihang, Kim, Daniel, Liu, Zhiwei, Luo, Zheheng, Yu, Yangyang, Cao, Yupeng, Deng, Zhiyang, Yao, Zhiyuan, Li, Haohang, Feng, Duanyu, Dai, Yongfu, Somasundaram, VijayaSai, Lu, Peng, Zhao, Yilun, Long, Yitao, Xiong, Guojun, Smith, Kaleb, Yu, Honghai, Lai, Yanzhao, Peng, Min, Nie, Jianyun, Suchow, Jordan W., Liu, Xiao-Yang, Wang, Benyou, Lopez-Lira, Alejandro, Huang, Jimin, Ananiadou, Sophia
Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \t
Externí odkaz:
http://arxiv.org/abs/2408.11878
Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a mixture of nodes with different neighbor patterns, exhibiting local n
Externí odkaz:
http://arxiv.org/abs/2408.09490
Autor:
Wang, Yuxin, Feng, Duanyu, Dai, Yongfu, Chen, Zhengyu, Huang, Jimin, Ananiadou, Sophia, Xie, Qianqian, Wang, Hao
Data serves as the fundamental foundation for advancing deep learning, particularly tabular data presented in a structured format, which is highly conducive to modeling. However, even in the era of LLM, obtaining tabular data from sensitive domains r
Externí odkaz:
http://arxiv.org/abs/2408.02927
Autor:
Chen, Yuyan, Wen, Zhihao, Fan, Ge, Chen, Zhengyu, Wu, Wei, Liu, Dayiheng, Li, Zhixu, Liu, Bang, Xiao, Yanghua
Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks,
Externí odkaz:
http://arxiv.org/abs/2407.04118
Autor:
Yin, Zichen, Zhang, Shuwei, He, Bin, Yang, Houpu, Chen, Zhengyu, Hu, Zhangwei, Shi, Yejiong, Xue, Ruizhi, Yang, Panqi, Ying, Yuzhe, Wang, Chengming, Wang, Shu, Xue, Ping
Label-free metabolic dynamics contrast is highly appealing but difficult to achieve in biomedical imaging. Interference offers a highly sensitive mechanism for capturing the metabolic dynamics of the subcellular scatterers. However, traditional inter
Externí odkaz:
http://arxiv.org/abs/2406.03798
Autor:
Yin, Zichen, He, Bin, Ying, Yuzhe, Zhang, Shuwei, Yang, Panqi, Chen, Zhengyu, Hu, Zhangwei, Shi, Yejiong, Xue, Ruizhi, Wang, Chengming, Wang, Shu, Wang, Guihuai, Xue, Ping
Pathological features are the gold standard for tumor diagnosis, guiding treatment and prognosis. However, standard histopathological process is labor-intensive and time-consuming, while frozen sections have lower accuracy. Dynamic full-field optical
Externí odkaz:
http://arxiv.org/abs/2404.19641
This paper studies the problem of distribution shifts on non-homophilous graphs Mosting existing graph neural network methods rely on the homophilous assumption that nodes from the same class are more likely to be linked. However, such assumptions of
Externí odkaz:
http://arxiv.org/abs/2403.10572
Autor:
Xie, Qianqian, Han, Weiguang, Chen, Zhengyu, Xiang, Ruoyu, Zhang, Xiao, He, Yueru, Xiao, Mengxi, Li, Dong, Dai, Yongfu, Feng, Duanyu, Xu, Yijing, Kang, Haoqiang, Kuang, Ziyan, Yuan, Chenhan, Yang, Kailai, Luo, Zheheng, Zhang, Tianlin, Liu, Zhiwei, Xiong, Guojun, Deng, Zhiyang, Jiang, Yuechen, Yao, Zhiyuan, Li, Haohang, Yu, Yangyang, Hu, Gang, Huang, Jiajia, Liu, Xiao-Yang, Lopez-Lira, Alejandro, Wang, Benyou, Lai, Yanzhao, Wang, Hao, Peng, Min, Ananiadou, Sophia, Huang, Jimin
LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2402.12659
Autor:
Chen, Zhengyu, Xiao, Teng, Kuang, Kun, Lv, Zheqi, Zhang, Min, Yang, Jinluan, Lu, Chengqiang, Yang, Hongxia, Wu, Fei
Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. The cardinal impetus underlying the severe
Externí odkaz:
http://arxiv.org/abs/2312.12475
Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefabricated graph augmentation and homophily assumptions. Thus, they fail
Externí odkaz:
http://arxiv.org/abs/2310.18884