Zobrazeno 1 - 10
of 809
pro vyhledávání: '"Zhang, Qianru"'
Effective spatio-temporal prediction frameworks play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, the presence of data noise and label sparsity in
Externí odkaz:
http://arxiv.org/abs/2410.10915
Large Vision-Language Models (LVLMs) have achieved remarkable progress on visual perception and linguistic interpretation. Despite their impressive capabilities across various tasks, LVLMs still suffer from the issue of hallucination, which involves
Externí odkaz:
http://arxiv.org/abs/2410.04107
Autor:
Zhang, Qianru, Yang, Peng, Yu, Junliang, Wang, Haixin, He, Xingwei, Yiu, Siu-Ming, Yin, Hongzhi
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Point-of-Interest (POI) recommendation systems. These advanced POI system
Externí odkaz:
http://arxiv.org/abs/2410.02191
Publikováno v:
Transactions on Knowledge Discovery from Data 2024
Nowadays, it becomes a common practice to capture some data of sports games with devices such as GPS sensors and cameras and then use the data to perform various analyses on sports games, including tactics discovery, similar game retrieval, performan
Externí odkaz:
http://arxiv.org/abs/2407.19686
Autor:
Zhang, Qianru, Wang, Haixin, Long, Cheng, Su, Liangcai, He, Xingwei, Chang, Jianlong, Wu, Tailin, Yin, Hongzhi, Yiu, Siu-Ming, Tian, Qi, Jensen, Christian S.
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques
Externí odkaz:
http://arxiv.org/abs/2405.09592
Publikováno v:
ICDE 2024
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing G
Externí odkaz:
http://arxiv.org/abs/2403.16656
Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviating the hallucination problem encountered by large language models. Giv
Externí odkaz:
http://arxiv.org/abs/2312.07049
Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction. However, most
Externí odkaz:
http://arxiv.org/abs/2306.10683
Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several k
Externí odkaz:
http://arxiv.org/abs/2305.03920
Autor:
Ren, Jianxiong, Zhang, Xiaoming, Si, Xingyong, Kong, Xiangjun, Cong, Jinyu, Wang, Pingping, Li, Xiang, Zhang, Qianru, Yao, Peifen, Li, Mengyao, Cai, Yuanqi, Sun, Zhaocai, Liu, Kunmeng, Wei, Benzheng
mRNA therapy is gaining worldwide attention as an emerging therapeutic approach. The widespread use of mRNA vaccines during the COVID-19 outbreak has demonstrated the potential of mRNA therapy. As mRNA-based drugs have expanded and their indications
Externí odkaz:
http://arxiv.org/abs/2303.00288