Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Feng Xingdong"'
All-metallic high-efficiency generalized Pancharatnam–Berry phase metasurface with chiral meta-atoms
Autor:
Cai Jixiang, Zhang Fei, Pu Mingbo, Chen Yan, Guo Yinghui, Xie Ting, Feng Xingdong, Ma Xiaoliang, Li Xiong, Yu Honglin, Luo Xiangang
Publikováno v:
Nanophotonics, Vol 11, Iss 9, Pp 1961-1968 (2022)
Metasurfaces based on the Pancharatnam–Berry (PB) phase have attracted significant attention in the domains of subwavelength optics and electromagnetics. Conventional theory predicts that the PB phase is exactly twice the rotation angle of the anis
Externí odkaz:
https://doaj.org/article/f3b587ce181a413481fa5aa8496dd0bc
Autor:
Wang, Caixing, Feng, Xingdong
The random feature (RF) approach is a well-established and efficient tool for scalable kernel methods, but existing literature has primarily focused on kernel ridge regression with random features (KRR-RF), which has limitations in handling heterogen
Externí odkaz:
http://arxiv.org/abs/2408.13591
In recent years, transfer learning has garnered significant attention in the machine learning community. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This
Externí odkaz:
http://arxiv.org/abs/2310.13966
Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some sp
Externí odkaz:
http://arxiv.org/abs/2310.08237
Autor:
Zhang, Tianqi, Wang, Yunshen, Feng, Xingdong, Zuo, Yizhou, Yu, Hannong, Bao, Hong, Jiang, Fan, Jiang, Shan
Publikováno v:
In iScience 20 September 2024 27(9)
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushing the initial samples from a reference distribution i
Externí odkaz:
http://arxiv.org/abs/2110.02787
Publikováno v:
In Journal of Econometrics January 2024 238(1)
Autor:
Cheng, Chao, Feng, Xingdong
It becomes an interesting problem to identify subgroup structures in data analysis as populations are probably heterogeneous in practice. In this paper, we consider M-estimators together with both concave and pairwise fusion penalties, which can deal
Externí odkaz:
http://arxiv.org/abs/2005.00248
Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed inpu
Externí odkaz:
http://arxiv.org/abs/2004.14227
We develop a constructive approach for $\ell_0$-penalized estimation in the sparse accelerated failure time (AFT) model with high-dimensional covariates. Our proposed method is based on Stute's weighted least squares criterion combined with $\ell_0$-
Externí odkaz:
http://arxiv.org/abs/2002.03318