Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

A review paper by scientists at the Beijing Institute of Technology summarized recent efforts and future potentials in the use of in vitro biological neural networks (BNNs) for the realization of ...

Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human ...

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 ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java. Artificial neural networks are a form of deep learning ...