9 citations · 18 across the 4 of their papers we have counts for
6 papers
Improved Dual Correlation Reduction Network
Yue Liu, Sihang Zhou, Xinwang Liu +2
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging…
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Chen Zhang, Siwei Wang, Wenxuan Tu +4
Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization whi…
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Mingjie Luo, Siwei Wang, Xinwang Liu +5
Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dr…
Deep Fusion Clustering Network
Wenxuan Tu, Sihang Zhou, Xinwang Liu +4
Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structu…
An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
Bingjie Yan, Xiangyan Tang, Boyi Liu +6
New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. By understanding the development trend of a regional…
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing
Bin Jiang, Wenxuan Tu, Chao Yang +1
Semantic segmentation for lightweight object parsing is a very challenging task, because both accuracy and efficiency (e.g., execution speed, memory footprint or computational comp…