Machine Learning; Deep Learning; Sparse Neural Networks; Dynamic Sparse Training
Résumé :
[en] 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. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
MOCANU, Decebal Constantin ; University of Luxembourg ; Department of Mathematics and Computer Science, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands. d.c.mocanu@tue.nl ; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands. d.c.mocanu@tue.nl
Mocanu, Elena ; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands ; Department of Mechanical Engineering, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands
Stone, Peter; Department of Computer Science, The University of Texas at Austin, 2317 Speedway, Stop D9500, Austin, TX, 78712-1757, USA
Nguyen, Phuong H; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands
Gibescu, Madeleine; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands
Liotta, Antonio ; Data Science Centre, University of Derby, Lonsdale House, Quaker Way, Derby, DE1 3HD, UK
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.
Date de publication/diffusion :
19 juin 2018
Titre du périodique :
Nature Communications
eISSN :
2041-1723
Maison d'édition :
Nature Publishing Group, Basingstoke, Hampshire, England
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