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

ABSTRACT: Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy.

SUBMITTER: Mocanu DC 

PROVIDER: S-EPMC6008460 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature