Modularity as a Means for Complexity Management in Neural Networks Learning

David Castillo-Bolado; Cayetano Guerra-Artal; Mario Hernandez-Tejera

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arXiv · 1902.09240

Modularity as a Means for Complexity Management in Neural Networks Learning

David Castillo-Bolado·Cayetano Guerra-Artal·Mario Hernandez-Tejera

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Abstract

Training a Neural Network (NN) with lots of parameters or intricate architectures creates undesired phenomena that complicate the optimization process. To address this issue we propose a first modular approach to NN design, wherein the NN is decomposed into a control module and several functional modules, implementing primitive operations. We illustrate the modular concept by comparing performances between a monolithic and a modular NN on a list sorting problem and show the benefits in terms of training speed, training stability and maintainability. We also discuss some questions that arise in modular NNs.

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David Castillo-Bolado, Cayetano Guerra-Artal, Mario Hernandez-Tejera. 2019-02-25. Modularity as a Means for Complexity Management in Neural Networks Learning. https://arxiv.org/abs/1902.09240

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