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- Tsinghua UniversityCN23 papers
- Chinese Academy of SciencesCN20 papers
- Ministry of Industry and Information TechnologyCN20 papers
- Xidian UniversityCN19 papers
- Xi'an Jiaotong UniversityCN18 papers
- University of Chinese Academy of SciencesCN14 papers
- Australian National UniversityAU12 papers
- Nanyang Technological UniversitySG12 papers
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- Moscow Institute of Physics and TechnologyRU11 papers
- Beijing Normal UniversityCN10 papers
- Centre National de la Recherche ScientifiqueFR10 papers
6 papers · 2 filters
Sparse PCA via -Norm Regularization for Unsupervised Feature Selection
Zhengxin Li, Feiping Nie, Jintang Bian +1
In the field of data mining, how to deal with high-dimensional data is an inevitable problem. Unsupervised feature selection has attracted more and more attention because it does n…
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
Wen Sun, Shiyu Lei, Lu Wang +2
Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4.0 benefits. In this pape…
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
Caixia Yan, Xiaojun Chang, Minnan Luo +4
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are p…
Extending Label Smoothing Regularization with Self-Knowledge Distillation
Ji-Yue Wang, Pei Zhang, Wen-feng Pang +1
Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the…
Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer
Qian Li, Qingyuan Hu, Yong Qi +3
Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. A…
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise
Yan Yan, Yuhong Guo
Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels that include both the true label and irrelevant noise labe…