Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Pengzhan Guo"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-16 (2022)
Abstract Career planning consists of a series of decisions that will significantly impact one’s life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term
Externí odkaz:
https://doaj.org/article/3d6bfebbbccb4aa7a933513dad22e083
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:5037-5050
This paper investigates the stochastic optimization problem with a focus on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network m
Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation syste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35648c849ccaec76f6055b1a3c8c52ba
Publikováno v:
ICDM
We investigate the stochastic optimization problem and develop a scalable parallel computing algorithm for deep learning tasks. The key of our study involves a reformation of the objective function for the stochastic optimization in neural network mo
Autor:
Pengzhan Guo, Hua Tang
Publikováno v:
Optik. 125:1227-1230
Zernike polynomial decompositions are used for investigating phase distortion induced by atmospheric turbulence in optical systems. Closed-form expression of the Zernike-coefficient variances is derived. The finite size of the receiver aperture is an