Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Zeyang Ye"'
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b91c67d63bc47ab052df1774565164f2
https://doi.org/10.1109/tkde.2020.3047894
https://doi.org/10.1109/tkde.2020.3047894
Publikováno v:
14th International Photonics and Optoelectronics Meetings (POEM 2022).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::657d714c3df3013c2f69ee312e653cf6
https://doi.org/10.1117/12.2672767
https://doi.org/10.1117/12.2672767
Publikováno v:
Current Psychology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eba2d3299a39c829001c487a44961e04
https://doi.org/10.1007/s12144-022-03221-9
https://doi.org/10.1007/s12144-022-03221-9
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 14:1-28
We enhance the mobile sequential recommendation (MSR) model and address some critical issues in existing formulations by proposing three new forms of the MSR from a multi-user perspective. The multi-user MSR (MMSR) model searches optimal routes for m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52df3a36f61343b55de0bf5f385dcf86
https://doi.org/10.1145/3360048
https://doi.org/10.1145/3360048
Publikováno v:
Monte Carlo Methods and Applications. 25:227-237
We introduce a parallel scheme for simulated annealing, a widely used Markov chain Monte Carlo (MCMC) method for optimization. Our method is constructed and analyzed under the classical framework of MCMC. The benchmark function for optimization is us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a15fadaf2266b643153dd524673ce72d
https://doi.org/10.1515/mcma-2019-2043
https://doi.org/10.1515/mcma-2019-2043
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 31:243-256
We speed up the solution of the mobile sequential recommendation (MSR) problem that requires searching optimal routes for empty taxi cabs through mining massive taxi GPS data. We develop new methods that combine parallel computing and the simulated a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bee913bf40a32e276cddd398f6adbb23
https://doi.org/10.1109/tkde.2018.2827047
https://doi.org/10.1109/tkde.2018.2827047
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
Understanding the Teaching Styles by an Attention based Multi-task Cross-media Dimensional modelling
Autor:
Yufeng Yin, Zeyang Ye, Yang Yao, Kehua Lei, Yan Huang, Jialie Shen, Suping Zhou, Xiang Li, Ying Zhang, Jia Jia
Teaching style plays an influential role in helping students to achieve academic success. In this paper, we explore a new problem of effectively understanding teachers' teaching styles. Specifically, we study 1) how to quantitatively characterize var
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a43d120282e5e6ee35e7f725adca4926
http://arxiv.org/abs/1911.07253
http://arxiv.org/abs/1911.07253
Publikováno v:
ICDM
In this paper, we develop an efficient parallelheuristic method for solving the global optimization problemassociated with the ridesharing system. Based on the carefullyformalized problem and objective function, we fully utilize theheuristic characte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5e6d8cc144ba4b68ec44facc15085f5
https://doi.org/10.1109/icdm.2019.00101
https://doi.org/10.1109/icdm.2019.00101