Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Yue Zuogong"'
Autor:
Yuan Ye, Liu Jun, Jin Dou, Yue Zuogong, Yang Tao, Chen Ruijuan, Wang Maolin, Xu Lei, Hua Feng, Guo Yuqi, Tang Xiuchuan, He Xin, Yi Xinlei, Li Dong, Yu Wenwu, Zhang Hai-Tao, Chai Tianyou, Sui Shaochun, Ding Han
Publikováno v:
National Science Open, Vol 2 (2023)
Traditional machine learning relies on a centralized data pipeline for model training in various applications; however, data are inherently fragmented. Such a decentralized nature of databases presents the serious challenge for collaboration: sending
Externí odkaz:
https://doaj.org/article/33d9065847d348a683f503ee3bf9891a
Autor:
Yue, Zuogong, Solo, Victor
We develop hard clustering based on likelihood rather than distance and prove convergence. We also provide simulations and real data examples.
Externí odkaz:
http://arxiv.org/abs/2409.06938
Low sampling frequency challenges the exact identification of the continuous-time (CT) dynamical system from sampled data, even when its model is identifiable. The necessary and sufficient condition is proposed -- which is built from Koopman operator
Externí odkaz:
http://arxiv.org/abs/2204.14021
Autor:
Yuan, Ye, Liu, Jun, Jin, Dou, Yue, Zuogong, Chen, Ruijuan, Wang, Maolin, Sun, Chuan, Xu, Lei, Hua, Feng, He, Xin, Yi, Xinlei, Yang, Tao, Zhang, Hai-Tao, Sui, Shaochun, Ding, Han
Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherently fragmented. Such a decentralized nature of these databases presents
Externí odkaz:
http://arxiv.org/abs/2107.07171
Publikováno v:
In Reliability Engineering and System Safety October 2023 238
Akademický článek
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Akademický článek
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Dynamic network reconstruction has been shown to be challenging due to the requirements on sparse network structures and network identifiability. The direct parametric method (e.g., using ARX models) requires a large amount of parameters in model sel
Externí odkaz:
http://arxiv.org/abs/1811.08677
Autor:
Wang, Yasen, Fang, Huazhen, Jin, Junyang, Ma, Guijun, He, Xin, Dai, Xing, Yue, Zuogong, Cheng, Cheng, Zhang, Hai-Tao, Pu, Donglin, Wu, Dongrui, Yuan, Ye, Gonçalves, Jorge, Kurths, Jürgen, Ding, Han
Publikováno v:
In Engineering October 2022 17:244-252
Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations to parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data
Externí odkaz:
http://arxiv.org/abs/1612.01963