The impact of sparsity and entropy criteria on neural network performance

Bryson Boreland (), Herb Kunze () and Kim Levere ()
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Bryson Boreland: University of Guelph
Herb Kunze: University of Guelph
Kim Levere: University of Guelph

Annals of Operations Research, 2025, vol. 346, issue 2, No 5, 827-838

Abstract: Abstract We explore the impact of adding entropy and sparsity criteria to a standard neural network cost function, by considering a variety of network types and applications. Measuring network performance via the testing error, we seek to answer the question: does including an entropy criterion and/or a sparsity criterion with some choice(s) of coefficient(s) produce a performance improvement of the network? The exploration suggests that the addition of a single one of these two criteria, with appropriate choice of coefficient, generates a performance improvement, and the inclusion of both criteria, with appropriate choice of coefficients, generates a further improvement. This suggestion reflects established results for parameter estimation inverse problems in a number of other settings.

Date: 2025
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DOI: 10.1007/s10479-024-05834-8

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