Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Tan, Yanchao"'
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Yu, Fengyuan, Zhu, Huabin, Yao, Binhui, Wang, Tao, Zheng, Xiaolin, Tan, Yanchao
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existi
Externí odkaz:
http://arxiv.org/abs/2403.16398
Graphs with abundant attributes are essential in modeling interconnected entities and improving predictions in various real-world applications. Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to
Externí odkaz:
http://arxiv.org/abs/2403.04780
Autor:
Liao, Xinting, Chen, Chaochao, Liu, Weiming, Zhou, Pengyang, Zhu, Huabin, Shen, Shuheng, Wang, Weiqiang, Hu, Mengling, Tan, Yanchao, Zheng, Xiaolin
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the mode
Externí odkaz:
http://arxiv.org/abs/2308.11646
Autor:
Du, Shide, Fang, Zihan, Lan, Shiyang, Tan, Yanchao, Günther, Manuel, Wang, Shiping, Guo, Wenzhong
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has bec
Externí odkaz:
http://arxiv.org/abs/2308.03666
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Zhu, Huabin, Tan, Yanchao, Wang, Jun, Qi, Yue
Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the clas
Externí odkaz:
http://arxiv.org/abs/2307.14384
The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks. Without proper denoising, RS models cannot effectively capture users' intrinsic prefe
Externí odkaz:
http://arxiv.org/abs/2204.08619
Combined visual and force feedback play an essential role in contact-rich robotic manipulation tasks. Current methods focus on developing the feedback control around a single modality while underrating the synergy of the sensors. Fusing different sen
Externí odkaz:
http://arxiv.org/abs/2202.08401
Autor:
Huang, Sujia, Xiao, Shunxin, Chen, Yuhong, Yang, Jinbin, Shi, Zhibin, Tan, Yanchao, Wang, Shiping
Publikováno v:
In Expert Systems With Applications 15 November 2024 254
Publikováno v:
In Neural Networks November 2024 179
Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interactions among users and items. Metric learning has been proposed to captu
Externí odkaz:
http://arxiv.org/abs/2103.14866