On the explainability of max-plus neural networks

Ikhlas Enaieh; Olivier Fercoq; García Ángel

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arXiv · 2605.00889

On the explainability of max-plus neural networks

Ikhlas Enaieh·Olivier Fercoq·García Ángel

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Abstract

We investigate the explanability properties of the recently proposed linear-min-max neural networks. At initialization, they can be interpreted as k-medoids with the infinity norm as a distance. Then, they are trained using subgradient descent to better fit the data. The model has been shown to be a universal approximator. Yet, we can trace the decision process because a single most activated neuron is responsible for the value of the output. Using this property, we designed a pixel fragility measure that determines whether changes to a single pixel may be responsible to a change in the classification output. Experiments on the PneumoniaMnist dataset show that this explanation for the output of the neural network compares favorably to SHAP and Integrated Gradient.

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BibTeX RIS

Ikhlas Enaieh, Olivier Fercoq, García Ángel. 2026-04-27. On the explainability of max-plus neural networks. https://arxiv.org/abs/2605.00889

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