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

Graph neural networks are very powerful tools. They have already found powerful applications in domains such as route planning, fraud detection, network optimization, and drug research.

Graph neural networks help to process and analyze complex graph-structured data, unlocking new possibilities across a wide range of applications.

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

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has introduced CGSchNet, a machine-learned coarse-grained (CG) model that can ...

Using this information, the model can then tell us the probability of a drug-protein interaction that we did not previously ...

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

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 ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.