Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, and recommendation systems). In this vein, ...

Skeleton-based recognition of human actions has received attention in recent years because of the popularity of 3-D acquisition sensors. Existing studies use 3-D skeleton data from video clips ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

A new study introduces brain-inspired architecture to artificial neural networks, offering smarter learning, lower environmental costs, and potential breakthroughs in understanding the human brain ...

Implementation of various neural graph classification model (not node classification) Training and test of various Graph Neural Networks (GNNs) models using graph classification datasets Input graph: ...