Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Abstract

Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.