Abstract: Graph convolutional networks (GCNs) excel in skeleton-based action recognition by modelling human body skeletons as spatiotemporal graphs. In current GCN-based approaches, the graph topology ...

Our demo for skeleton based action recognition: ST-GCN is able to exploit local pattern and correlation from human skeletons. Below figures show the neural response magnitude of each node in the last ...

Abstract: Graph Convolutional Networks (GCNs) have demonstrated significant efficacy in graph representation learning. Existing approaches often rely on stacking multiple GCN layers to incorporate ...

Department of Chemistry and Biochemistry, University of Wisconsin─Eau Claire, Eau Claire, Wisconsin 54702, United States ...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...

Recent studies indicate that microorganisms are crucial for maintaining human health. Dysbiosis, or an imbalance in these microbial communities, is strongly linked to a variety of human diseases.

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods ...