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pro vyhledávání: '"Hu, Lingjing"'
Inferring causal structures from time series data is the central interest of many scientific inquiries. A major barrier to such inference is the problem of subsampling, i.e., the frequency of measurement is much lower than that of causal influence. T
Externí odkaz:
http://arxiv.org/abs/2305.05276
The prediction and selection of lesion features are two important tasks in voxel-based neuroimage analysis. Existing multivariate learning models take two tasks equivalently and optimize simultaneously. However, in addition to lesion features, we obs
Externí odkaz:
http://arxiv.org/abs/2007.08740
Autor:
Sun, Xinwei, Xu, Yilun, Cao, Peng, Kong, Yuqing, Hu, Lingjing, Zhang, Shanghang, Wang, Yizhou
Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality with a large amount of data, which leads to a crucial problem of semi-s
Externí odkaz:
http://arxiv.org/abs/2007.06793
Akademický článek
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Recent studies found that in voxel-based neuroimage analysis, detecting and differentiating "procedural bias" that are introduced during the preprocessing steps from lesion features, not only can help boost accuracy but also can improve interpretabil
Externí odkaz:
http://arxiv.org/abs/1807.08125
In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during th
Externí odkaz:
http://arxiv.org/abs/1705.09249
Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are applied to select the optimal features and achieve high diagnosis accurac
Externí odkaz:
http://arxiv.org/abs/1503.07508
Akademický článek
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Publikováno v:
In Archives of Oral Biology April 2014 59(4):393-399
Publikováno v:
Therapeutic Innovation & Regulatory Science; Jul2022, Vol. 56 Issue 4, p561-571, 11p