Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

Uses for Neural Networks A major reason for the popularity of neural networks is that they can solve real-world problems and make intelligent decisions with little to no human intervention.

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

Researchers are training neural networks to make decisions more like humans would. This science of human decision-making is only just being applied to machine learning, but developing a neural ...

The future of software development isn’t just about who codes faster—it’s about who can express intent more naturally.

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates.

A simulation that uses machine learning predicts neural-circuit function in the fly brain from the connectivity between neurons.

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.