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

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.

Bibliographic Details
Main Authors: Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta
Format: Article
Language:English
Published: Nature Publishing Group 2018-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-04316-3