A team of researchers from Ochsner Health recently published an insightful article in the International Forum of Allergy & Rhinology exploring the application of convolutional neural networks (CNNs) ...

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. Neural networks are the ...

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...

Researchers have developed TD-MCL, a spiking neural network framework inspired by how infant brains grow and prune synapses. The method enables AI to learn new tasks without forgetting old ones while ...

Artificial intelligence (AI) has made tremendous progress since its inception, and neural networks are usually part of that advancement. Neural networks that apply weights to variables in AI models ...

From Facebook friend circles to hidden influencer groups, community detection in social networks is evolving fast. Researchers are combining deep learning, graph neural networks, and advanced ...

Neural-network processors accelerate AI program execution while development tools help you get to market fast.