Zobrazeno 1 - 10
of 3 999
pro vyhledávání: '"GAO, Kun"'
Autor:
Ye, Dayong, Zhu, Tianqing, Zhu, Congcong, Wang, Derui, Gao, Kun, Shi, Zewei, Shen, Sheng, Zhou, Wanlei, Xue, Minhui
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners. However, one important area that has been largely overlooked in the research of unle
Externí odkaz:
http://arxiv.org/abs/2312.15910
Automated vehicles are envisioned to navigate safely in complex mixed-traffic scenarios alongside human-driven vehicles. To promise a high degree of safety, accurately predicting the maneuvers of surrounding vehicles and their future positions is a c
Externí odkaz:
http://arxiv.org/abs/2312.12123
Autor:
Bai, Long, Yao, Shilong, Gao, Kun, Huang, Yanjun, Tang, Ruijie, Yan, Hong, Meng, Max Q. -H., Ren, Hongliang
Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method. Firs
Externí odkaz:
http://arxiv.org/abs/2310.09937
We propose a framework that can incrementally expand the explanatory temporal logic rule set to explain the occurrence of temporal events. Leveraging the temporal point process modeling and learning framework, the rule content and weights will be gra
Externí odkaz:
http://arxiv.org/abs/2308.06094
Publikováno v:
Shipin Kexue, Vol 45, Iss 14, Pp 172-178 (2024)
With a view to investigating the effect of wheat sprouting degree on the cooking quality, textural characteristics and sensory evaluation of dried whole wheat noodles, whole wheat flours with different degrees of wheat sprouting (bulging, revealing,
Externí odkaz:
https://doaj.org/article/7c2100fbdb0c44a2893870ffb2433c85
Autor:
Li, Hang1,2 (AUTHOR), Gao, Kun1,2 (AUTHOR), Guo, Huan3 (AUTHOR), Li, Rongfeng1,4 (AUTHOR) rongfengli@qdio.ac.cn, Li, Guantian1,2 (AUTHOR) rongfengli@qdio.ac.cn
Publikováno v:
Polymers (20734360). Sep2024, Vol. 16 Issue 17, p2402. 42p.
Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a novel differentiable inductive logic programming (ILP) model, called diffe
Externí odkaz:
http://arxiv.org/abs/2204.13570
Autor:
Gao, Kun, Liu, Xiaolong, Ren, Pengfei, Chen, Haoyu, Zhen, Tao, Xie, Liang, Li, Zhongkui, Yan, Ye, Zhang, Haoyang, Yin, Erwei
Publikováno v:
In Knowledge-Based Systems 25 November 2024 304
Publikováno v:
In Sustainable Cities and Society 15 November 2024 115
Publikováno v:
In International Journal of Biological Macromolecules November 2024 280 Part 1