Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and reasoning models like ChatGPT. The principle: during a training phase, the ...

Researchers from the University of Campinas (Campinas, Brazil) and the Waters Research Center (Budapest, Hungary) introduced ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

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 ...

Graph Neural Networks (GNNs) have demonstrated strengths in areas such as recommendation systems, natural language processing, computational chemistry, and bioinformatics. Popular training ...

Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is partitioned across ...

Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of ...