Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Graphs are, quite simply, a universal ...

Nanoengineers have developed new deep learning models that can accurately predict the properties of molecules and crystals. The models can enable researchers to rapidly scan the nearly-infinite ...

“Over the last year, we’ve evolved our Cloud Spanner Graph and Vertex AI to handle the ‘dual nature’ of telcos: the need for high-speed, real-time response for alarm correlation, combined with deep, ...

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, ...