Graph Convolutional Networks News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Graph convolutional networks (GCNs) are widely used in skeleton-based action recognition. It is known that the graph topology is a vital part in GCNs, and different kinds of graph topologies have been proposed for skeleton-based action recognition, mostly based on a predefined topology and a dynamically learned one. The predefined topology is based on the human intuition for skeleton (the connectivity of joints) and has not been investigated whether it is optimal. In this paper, we focus on investigating this static graph topology and propose to generate a learned static graph topology for ske...

Graph representation learning aims at mapping a graph into a lower-dimensional feature space. Deep attributed graph representation, utilizing deep learning models on the graph structure and attributes, shows its significance in mining complex relational data. Most existing deep attributed graph representation models assume graph attributes in a single-attributed view. However, rich information in real-world applications demands the ability to handle multiple attributed views. For example, in social network users' profiles and posts represent two distinct attributed views. A single-attribu...

Research Scientist: Machine Learning job in Great Baddow, Chelmsford with BAE Systems Digital Intelligence. Apply Today.

Research Scientist: Machine Learning job in Great Baddow, Chelmsford with BAE Systems Digital Intelligence. Apply Today.

Research Scientist: Machine Learning job in Great Baddow, Chelmsford with BAE Systems Digital Intelligence. Apply Today.