Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Li Linguo"'
Publikováno v:
Sichuan jingshen weisheng, Vol 35, Iss 2, Pp 188-193 (2022)
ObjectiveTo evaluate the methodological quality of systematic review / Meta analysis (SR/MA) of intervention randomized controlled trial (RCT) published in the Sichuan Mental Health.MethodsThe literature databases such as Wanfang Data, Chin
Externí odkaz:
https://doaj.org/article/fd72ce75558d4f818e6da92c50fa4e60
Autor:
Huang, Xiaoyang, Yang, Jiancheng, Wang, Yanjun, Chen, Ziyu, Li, Linguo, Li, Teng, Ni, Bingbing, Zhang, Wenjun
Publikováno v:
published in the Tenth International Conference on Learning Representations (ICLR 2022)
3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In this study, we prese
Externí odkaz:
http://arxiv.org/abs/2203.10259
In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary supervision signal. CrosSCLR consists of both single-view contrastive lea
Externí odkaz:
http://arxiv.org/abs/2104.14466
Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CN
Externí odkaz:
http://arxiv.org/abs/1910.08878
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making. For classification tasks, prior studies on UQ a
Externí odkaz:
http://arxiv.org/abs/1909.06030
Autor:
Yang, Jiancheng, Zhang, Qiang, Ni, Bingbing, Li, Linguo, Liu, Jinxian, Zhou, Mengdie, Tian, Qi
Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers
Externí odkaz:
http://arxiv.org/abs/1904.03375
Publikováno v:
Computers, Materials & Continua; 2024, Vol. 80 Issue 2, p2049-2063, 15p
Publikováno v:
In Construction and Building Materials 1 November 2021 306
Publikováno v:
Journal of Coastal Research, 2020 Apr 01, 991-995.
Externí odkaz:
https://www.jstor.org/stable/48748842
Autor:
Li, Shujing, Li, Linguo *
Publikováno v:
In Optik February 2021 227