Zobrazeno 1 - 10
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pro vyhledávání: '"Wang, YuLing"'
Autor:
Lu, Kangkang, Yu, Yanhua, Huang, Zhiyong, Li, Jia, Wang, Yuling, Liang, Meiyu, Qin, Xiting, Ren, Yimeng, Chua, Tat-Seng, Wang, Xidian
Graph Neural Networks (GNNs) have garnered significant scholarly attention for their powerful capabilities in modeling graph structures. Despite this, two primary challenges persist: heterogeneity and heterophily. Existing studies often address heter
Externí odkaz:
http://arxiv.org/abs/2410.13373
Autor:
Wang, Yuling, Tian, Changxin, Hu, Binbin, Yu, Yanhua, Liu, Ziqi, Zhang, Zhiqiang, Zhou, Jun, Pang, Liang, Wang, Xiao
Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully impl
Externí odkaz:
http://arxiv.org/abs/2403.04260
Autor:
Wang, Yuling, Wang, Xiao, Huang, Xiangzhou, Yu, Yanhua, Li, Haoyang, Zhang, Mengdi, Guo, Zirui, Wu, Wei
Publikováno v:
[C]//Proceedings of the 32th international joint conference on artificial intelligence. 2023: 2343-2351
Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data. Understanding the user intents from behavior data is the key to recommender systems, which pos
Externí odkaz:
http://arxiv.org/abs/2403.03714
Autor:
Guo, Zirui, Xia, Lianghao, Yu, Yanhua, Wang, Yuling, Yang, Zixuan, Wei, Wei, Pang, Liang, Chua, Tat-Seng, Huang, Chao
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural Networks (GNNs) have emerged as promising GSL solutions, utilizing rec
Externí odkaz:
http://arxiv.org/abs/2402.15183
Autor:
Jiang, Siyuan, Ding, Yan, Wang, Yuling, Xu, Lei, Dai, Wenli, Chang, Wanru, Zhang, Jianfeng, Yu, Jie, Zhou, Jianqiao, Zhang, Chunquan, Liang, Ping, Kong, Dexing
Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules. However, it relies heavily on the expertise and clin
Externí odkaz:
http://arxiv.org/abs/2310.06339
Publikováno v:
ICCV 2023
Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of the trade-off between quality and efficiency, e.g., MipNeRF presents fine-detailed and anti-aliased renderings but takes days for training, while Instant-ngp
Externí odkaz:
http://arxiv.org/abs/2307.11335
It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is
Externí odkaz:
http://arxiv.org/abs/2205.12076
Autor:
Wang, Yuling, Chen, Shijie, Tian, Xin, Lin, Yuan, Han, Dongqi, Yao, Ping, Xu, Hang, Wang, Yuanyuan, Zhao, Jie
Publikováno v:
In International Journal of Medical Informatics November 2024 191
Autor:
Pang, Haiyu, Yin, Jiahui, Li, Zhaoai, Gong, Jian, Liu, Qing, Wang, Yuling, Wang, Juntao, Xia, Zhijun, Liu, Jingyi, Si, Mingyu, Dang, Le, Fang, Jiaqi, Lu, Linli, Qiao, Youlin, Zhu, Lan
Publikováno v:
In European Journal of Obstetrics & Gynecology and Reproductive Biology October 2024 301:210-215