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Sparse Super-Regular Networks
Conference proceeding   Open access

Sparse Super-Regular Networks

Andrew W. E McDonald and Ali Shokoufandeh
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp 1764-1770
Dec 2019
DOI: https://doi.org/10.1109/ICMLA.2019.00286
url
http://arxiv.org/abs/2201.01363View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Bipartite graph Computer science deep learning expander graphs graph theory Machine learning Neural networks Sparse matrices sparse-neural networks super regularity Task analysis x-nets
It has been argued by Thom and Palm that sparsely-connected neural networks (SCNs) show improved performance over fully-connected networks (FCNs). Super-regular networks (SRNs) are neural networks composed of a set of stacked sparse layers of (epsilon, delta)-super-regular pairs, and randomly permuted node order. Using the Blow-up Lemma, we prove that as a result of the individual super-regularity of each pair of layers, SRNs guarantee a number of properties that make them suitable replacements for FCNs for many tasks. These guarantees include edge uniformity across all large-enough subsets, minimum node in-and out-degree, input-output sensitivity, and the ability to embed pre-trained constructs. Indeed, SRNs have the capacity to act like FCNs, and eliminate the need for costly regularization schemes like Dropout. We show that SRNs perform similarly to X-Nets via readily reproducible experiments, and offer far greater guarantees and control over network structure.

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3 citations in Scopus

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