5 papers · 1 filter
A Novel Graph-Sequence Learning Model for Inductive Text Classification
Zuo Wang, Ye Yuan
Text classification plays an important role in various downstream text-related tasks, such as sentiment analysis, fake news detection, and public opinion analysis. Recently, text c…
Jensen-Shannon Divergence Message-Passing for Rich-Text Graph Representation Learning
Zuo Wang, Ye Yuan
In this paper, we investigate how the widely existing contextual and structural divergence may influence the representation learning in rich-text graphs. To this end, we propose Je…
A Multi-scale Fused Graph Neural Network with Inter-view Contrastive Learning for Spatial Transcriptomics Data Clustering
Jianping Mei, Siqi Ai, Ye Yuan
Spatial transcriptomics enables genome-wide expression analysis within native tissue context, yet identifying spatial domains remains challenging due to complex gene-spatial intera…
Condensation-Concatenation Framework for Dynamic Graph Continual Learning
Tingxu Yan, Ye Yuan
Dynamic graphs are prevalent in real-world scenarios, where continuous structural changes induce catastrophic forgetting in graph neural networks (GNNs). While continual learning h…
CARI4D: Category Agnostic 4D Reconstruction of Human-Object Interaction
Xianghui Xie, Bowen Wen, Yan Chang +5
Accurate capture of human-object interaction from ubiquitous sensors like RGB cameras is important for applications in human understanding, gaming, and robot learning. However, inf…