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 ...

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 ...

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, ...

There are several classical statistics techniques for regression problems. Neural regression solves a regression problem using a neural network. This article is the second in a series of four articles ...

To overcome such inherent challenges with graph neural networks and improve recommendation abilities, LinkedIn has created a process it calls Performance-Adaptive Sampling Strategy (PASS). that ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...