SymRefine: A symbolic regression approach for refining and compressing neural networks.

Wei Wei, Qiang Lu, Can Huang, David Lee, Jake Luo

Abstract read

In one paragraph

Article in Neurocomputing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Wei WeiCollege of Artificial Intelligence, China University of Petroleum, Beijing 102249, China.ORCID 0009-0009-9533-5770

Qiang LuCollege of Artificial Intelligence, China University of Petroleum, Beijing 102249, China.ORCID 0000-0001-8217-2305

Can HuangInstitute for Computational and Mathematical Engineering, Stanford University, CA 94305, USA.

David LeePetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China.

Jake LuoDepartment of Health Sciences and Administration, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA.

Funding

Clinical and Translational Science AwardUL1TR001436 · NCATS · MEDICAL COLLEGE OF WISCONSIN · PI FREED, JULIE K · 2015 to 2025

$47.5M

NCATS NIH HHS UL1 TR001436

6 · The paper itself

Abstract

Traditional methods for compressing neural networks (NN), such as pruning and distillation, primarily focus on reducing the size of the NN, often at the expense of accuracy. To overcome this limitation, we propose a novel refining NN method based on symbolic regression called SymRefine. SymRefine constructs a Cartesian genetic programming linear method (LCGP) to identify the mathematical expression that represents the relationship between the high-dimensional input and output of a selected module within the NN. The effectiveness of SymRefine was tested using two types of NN benchmarks: multilayer perceptron NNs (MLP) and convolutional NNs (CNN). Experimental results show that, compared with NN pruning and NN distillation, SymRefine can effectively reduce the number of the NN parameters while maintaining or even enhancing inference accuracy. This research demonstrates the potential of SymRefine as a novel tool for creating more efficient and accurate neural networks.

Indexed as

Cartesian genetic programmingNeural network compressionNeural network refinementSymbolic regression

Identifiers

PMID41953691

DOI10.1016/j.neucom.2026.132719

PMCPMC13056368

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.