Leveraging all the pieces of the development kit is Arm Neural Super Sampling (NSS) – an AI-powered graphics upscaling engine. It builds on the foundation laid by Arm Accuracy Super Resolution (ASR), ...

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

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 ...

SINGAPORE - Singapore biotech firm Nanyang Biologics is expanding and diversifying its revenue stream beyond discovering ...

GNNs extend the foundational ideas of Convolutional Neural Networks (CNNs) to graph data. While CNNs capture spatial locality in grid-like data (for example, images) through convolutional kernels, ...

Arriving at this graph neural network destination took the combined work of Google as well as Amazon, Waymo, and Sea AI Lab, but now provides Google Maps with a far more accurate ETA and the ability ...

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.

This style of neural network is also known as a cyclical graph. The backward movement opens up a variety of more sophisticated learning techniques, and also makes RNNs more complex than some other ...