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Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity Inspired by Network Science.
Decebal Constantin Mocanu, Elena
Mocanu, Peter Stone, Phuong
H. Nguyen, Madeleine Gibescu, and Antonio
Liotta.
Nature Communications, 9(2383), June 2018.
Official version from Publisher's
Webpage.
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 (Erdos-Renyi 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.
@article{NatureComm18,
AUTHOR = {Decebal Constantin Mocanu and Elena Mocanu and Peter Stone and Phuong H.\ Nguyen and Madeleine Gibescu and Antonio Liotta},
TITLE = {Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity Inspired by Network Science},
JOURNAL={Nature Communications},
YEAR={2018},
volume=9,
number=2383,
month={June},
DOI={10.1038/s41467-018-04316-3},
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 (Erdos-Renyi 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.},
wwwnote={Official version from <a href="https://www.nature.com/articles/s41467-018-04316-3.pdf">Publisher's Webpage</a>.},
}
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