Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks.

But as mobile hardware advances, Machine Learning (ML) techniques, particularly Graph Neural Networks (GNNs), are emerging as a powerful, efficient alternative to emulate physics on mobile. GNNs are ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at ...