Zobrazeno 1 - 10
of 452
pro vyhledávání: '"Huang, JianWen"'
Autor:
Wei, Qiusen, Huang, Guoheng, Yuan, Xiaochen, Chen, Xuhang, Zhong, Guo, Huang, Jianwen, Huang, Jiajie
Medical landmark detection is crucial in various medical imaging modalities and procedures. Although deep learning-based methods have achieve promising performance, they are mostly designed for specific anatomical regions or tasks. In this work, we p
Externí odkaz:
http://arxiv.org/abs/2404.01194
This paper concentrates on the recovery of block-sparse signals, which is not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitrary distributed all over the vector, from linear measurements. We esta
Externí odkaz:
http://arxiv.org/abs/2006.06344
In this paper, we bring forward a completely perturbed nonconvex Schatten $p$-minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based on the restricted isometry property generalizes the investigation to
Externí odkaz:
http://arxiv.org/abs/2006.06283
Compressed sensing shows that a sparse signal can stably be recovered from incomplete linear measurements. But, in practical applications, some signals have additional structure, where the nonzero elements arise in some blocks. We call such signals a
Externí odkaz:
http://arxiv.org/abs/2006.06160
In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery. We obtain sufficient conditions $\delta_{tr}
Externí odkaz:
http://arxiv.org/abs/2003.04766
In this paper, asymptotic expansions of the distributions and densities of powered extremes for Maxwell samples are considered. The results show that the convergence speeds of normalized partial maxima relies on the powered index. Additionally, compa
Externí odkaz:
http://arxiv.org/abs/2003.03735
Autor:
Huang, Jianwen
Generalized Maxwell distribution is an extension of the classic Maxwell distribution. In this paper, we concentrate on the joint distributional asymptotics of normalized maxima and minima. Under optimal normalizing constants, asymptotic expansions of
Externí odkaz:
http://arxiv.org/abs/2003.03740
Previous work regarding low-rank matrix recovery has concentrated on the scenarios in which the matrix is noise-free and the measurements are corrupted by noise. However, in practical application, the matrix itself is usually perturbed by random nois
Externí odkaz:
http://arxiv.org/abs/2003.03180
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Akademický článek
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