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of 52
pro vyhledávání: '"Zhang, Xiatian"'
Purpose:Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting
Externí odkaz:
http://arxiv.org/abs/2312.13776
Autor:
Zhang, Xiatian, Zheng, Sisi, Shum, Hubert P. H., Zhang, Haozheng, Song, Nan, Song, Mingkang, Jia, Hongxiao
Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design
Externí odkaz:
http://arxiv.org/abs/2311.10463
Surgical workflow anticipation can give predictions on what steps to conduct or what instruments to use next, which is an essential part of the computer-assisted intervention system for surgery, e.g. workflow reasoning in robotic surgery. However, cu
Externí odkaz:
http://arxiv.org/abs/2208.03824
Parkinson's disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience
Externí odkaz:
http://arxiv.org/abs/2207.06828
Autor:
Xing, Hao, Pei, Junling, Song, Zhijie, Liu, Guangxian, Wu, Wei, Wen, Jinfeng, Zhang, Xiatian, Xu, Zeqi, Yang, Xi
Publikováno v:
In LITHOS September 2024 480-481
For high-dimensional data, there are huge communication costs for distributed GBDT because the communication volume of GBDT is related to the number of features. To overcome this problem, we propose a novel gradient boosting algorithm, the Gradient B
Externí odkaz:
http://arxiv.org/abs/2011.05022
Cities are living systems where urban infrastructures and their functions are defined and evolved due to population behaviors. Profiling the cities and functional regions has been an important topic in urban design and planning. This paper studies a
Externí odkaz:
http://arxiv.org/abs/1707.04210
In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems. As a gradient-based optimization method, GSA makes use of the information from the minimize
Externí odkaz:
http://arxiv.org/abs/1611.03608
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Akademický článek
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