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CASIA OpenIR

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Indirect estimation of pediatric reference interval via density graph deep embedded clustering 期刊论文
COMPUTERS IN BIOLOGY AND MEDICINE, 2024, 卷号: 169, 页码: 10
作者:  Zheng, Jianguo;  Tang, Yongqiang;  Peng, Xiaoxia;  Zhao, Jun;  Chen, Rui;  Yan, Ruohua;  Peng, Yaguang;  Zhang, Wensheng
收藏  |  浏览/下载：4/0  |  提交时间：2024/03/27
Reference interval  Reference interval  Indirect estimation  Indirect estimation  Machine learning  Machine learning  Deep neural networks  Deep neural networks  Graph clustering  Graph clustering  
Adaptive-weighted deep multi-view clustering with uniform scale representation 期刊论文
NEURAL NETWORKS, 2024, 卷号: 171, 页码: 114-126
作者:  Chen, Rui;  Tang, Yongqiang;  Zhang, Wensheng;  Feng, Wenlong
收藏  |  浏览/下载：33/0  |  提交时间：2024/02/21
Multi-view clustering  Deep clustering  Adaptive-weighted learning  Uniform scale representation  
IDO: Instance dual-optimization for weakly supervised object detection 期刊论文
APPLIED INTELLIGENCE, 2023, 页码: 18
作者:  Ren, Zhida;  Tang, Yongqiang;  Zhang, Wensheng
收藏  |  浏览/下载：31/0  |  提交时间：2023/11/17
Deep learning  Weakly supervised learning  Object detection  Multiple instance learning  
Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 8, 页码: 9357-9373
作者:  Tang, Yongqiang;  Xie, Yuan;  Zhang, Wensheng
收藏  |  浏览/下载：43/0  |  提交时间：2023/11/17
Affine subspace  low-rank representation  low-rank tensor  multi-view learning  subspace clustering  
Constrained Maximum Cross-Domain Likelihood for Domain Generalization 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 15
作者:  Lin, Jianxin;  Tang, Yongqiang;  Wang, Junping;  Zhang, Wensheng
收藏  |  浏览/下载：80/0  |  提交时间：2023/11/17
Optimization  Feature extraction  Metalearning  Entropy  Training  Hospitals  Task analysis  Distribution shift  domain adaptation  domain generalization  domain-invariant representation  joint distribution alignment  
Meta-path infomax joint structure enhancement for multiplex network representation learning 期刊论文
KNOWLEDGE-BASED SYSTEMS, 2023, 卷号: 275, 页码: 14
作者:  Yuan, Ruiwen;  Wu, Yajing;  Tang, Yongqiang;  Wang, Junping;  Zhang, Wensheng
收藏  |  浏览/下载：32/0  |  提交时间：2023/11/17
Multiplex network  Graph neural network  Network representation learning  Complementary information  Graph structure learning  
Semisupervised Progressive Representation Learning for Deep Multiview Clustering 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 15
作者:  Chen, Rui;  Tang, Yongqiang;  Xie, Yuan;  Feng, Wenlong;  Zhang, Wensheng
收藏  |  浏览/下载：67/0  |  提交时间：2023/11/17
Representation learning  Training  Data models  Task analysis  Complexity theory  Semisupervised learning  Optimization  Deep clustering  multiview clustering  progressive sample learning  semisupervised learning  
Open set domain adaptation with latent structure discovery and kernelized classifier learning 期刊论文
NEUROCOMPUTING, 2023, 卷号: 531, 页码: 125-139
作者:  Tang, Yongqiang;  Tian, Lei;  Zhang, Wensheng
收藏  |  浏览/下载：44/0  |  提交时间：2023/11/17
Open set domain adaptation  Adaptive graph learning  Latent structure discovery  Kernelized classifier learning  
Class-Oriented Self-Learning Graph Embedding for Image Compact Representation 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 1, 页码: 74-87
作者:  Hu, Liangchen;  Dai, Zhenlei;  Tian, Lei;  Zhang, Wensheng
收藏  |  浏览/下载：149/0  |  提交时间：2023/03/20
Sparse matrices  Manifolds  Machine learning algorithms  Laplace equations  Heuristic algorithms  Data models  Data mining  Adaptive graph learning  separability examination  marginal information preserving  L-2,L-p-norm sparsity  compact representation  
Partial Domain Adaptation by Progressive Sample Learning of Shared Classes 期刊论文
Neural Processing Letters, 2022, 卷号: 0, 期号: 0, 页码: 0
作者:  Lei, Tian;  Yongqiang, Tang;  Wensheng, Zhang
Adobe PDF(912Kb)  |  收藏  |  浏览/下载：185/50  |  提交时间：2022/04/06
Partial domain adaptation  Domain adaptation  Transfer learning  Self-paced learning  Low-dimensional subspace learning