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. 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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Mocanu DC,Mocanu E,Stone P,Nguyen PH,Gibescu M,Liotta Adoi
10.1038/s41467-018-04316-3subject
Has Abstractpub_date
2018-06-19 00:00:00pages
2383issue
1issn
2041-1723pii
10.1038/s41467-018-04316-3journal_volume
9pub_type
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