Zobrazeno 1 - 10
of 2 411
pro vyhledávání: '"Ju, Wei"'
Autor:
Luo, Junyu, Gu, Yiyang, Luo, Xiao, Ju, Wei, Xiao, Zhiping, Zhao, Yusheng, Yuan, Jingyang, Zhang, Ming
Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing approaches predominantly focus on Euclidean data, such as images and videos
Externí odkaz:
http://arxiv.org/abs/2410.16606
Supervised fine-tuning (SFT) is crucial in adapting large language models (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practical applications, which poses a severe challenge for SFT in yielding s
Externí odkaz:
http://arxiv.org/abs/2410.14745
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
Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in real-world scenarios, labeled graph
Externí odkaz:
http://arxiv.org/abs/2407.14081
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:
Ju, Wei, Wang, Yifan, Qin, Yifang, Mao, Zhengyang, Xiao, Zhiping, Luo, Junyu, Yang, Junwei, Gu, Yiyang, Wang, Dongjie, Long, Qingqing, Yi, Siyu, Luo, Xiao, Zhang, Ming
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challen
Externí odkaz:
http://arxiv.org/abs/2405.11868
Autor:
Ju, Wei, Mao, Zhengyang, Yi, Siyu, Qin, Yifang, Gu, Yiyang, Xiao, Zhiping, Wang, Yifan, Luo, Xiao, Zhang, Ming
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networ
Externí odkaz:
http://arxiv.org/abs/2405.04773
Autor:
Ju, Wei, Yi, Siyu, Wang, Yifan, Xiao, Zhiping, Mao, Zhengyang, Li, Hourun, Gu, Yiyang, Qin, Yifang, Yin, Nan, Wang, Senzhang, Liu, Xinwang, Luo, Xiao, Yu, Philip S., Zhang, Ming
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Ne
Externí odkaz:
http://arxiv.org/abs/2403.04468
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-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural networks demonstrate proficiency in modeling this type of data, their success
Externí odkaz:
http://arxiv.org/abs/2402.00447