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

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 ...

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 ...

Artificial Neural Networks: Learning by Doing Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

Feedforward neural networks (FNNs): A descendent of recurrent neural networks, FNN’s do not use any type of cycle or loop to process data and develop a model.

The standard “back-propagation” training technique for deep neural networks requires matrix multiplication, an ideal workload for GPUs. With SLIDE, Shrivastava, Chen and Medini turned neural network ...

The artificial intelligence (AI) algorithms that drive these recommendations use a type of technology known as a Graph Neural Network, which is based on graphs: mathematical structures made up of ...

A review of what Google's Neural Matching Algorithm might be and how it might impact SEO.