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

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

As summer winds down, many of us in continental Europe are heading back north. The long return journeys from the beaches of ...

Google LLC today detailed RigL, an algorithm developed by its researchers that makes artificial intelligence models more hardware-efficient by shrinking them. Neural networks are made up of so ...

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 ...

A new technical paper titled “Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware” was published by researchers at Purdue University, Pennsylvania State ...

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.

The algorithm uses supervised learning with known histopathology diagnoses (malignant and nonmalignant) as the labels for algorithm training. MIA3G is a classification deep feedforward neural network ...

Scientists from Tomsk Polytechnic University, together with their colleagues, analyzed various methods of planning experiments to determine the optimal technological parameters of polymer scaffold ...

On a more basic level, [Gigante] did just that, teaching a neural network to play a basic driving game with a genetic algorithm. The game consists of a basic top-down 2D driving game.