Robustness in sparse artificial neural networks trained with adaptive topology

• URL: http://arxiv.org/abs/2602.21961v1
• Date: Wed, 25 Feb 2026 14:44:15 GMT
• Title: Robustness in sparse artificial neural networks trained with adaptive topology
• Authors: Bendegúz Sulyok, Gergely Palla, Filippo Radicchi, Santo Fortunato,
• Abstract summary: We focus on a simple yet effective architecture consisting of three sparse layers with 99% sparsity followed by a dense layer, applied to image classification tasks such as MNIST and Fashion MNIST.<n>By updating the topology of the sparse layers between each epoch, we achieve competitive accuracy despite the significantly reduced number of weights.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the robustness of sparse artificial neural networks trained with adaptive topology. We focus on a simple yet effective architecture consisting of three sparse layers with 99% sparsity followed by a dense layer, applied to image classification tasks such as MNIST and Fashion MNIST. By updating the topology of the sparse layers between each epoch, we achieve competitive accuracy despite the significantly reduced number of weights. Our primary contribution is a detailed analysis of the robustness of these networks, exploring their performance under various perturbations including random link removal, adversarial attack, and link weight shuffling. Through extensive experiments, we demonstrate that adaptive topology not only enhances efficiency but also maintains robustness. This work highlights the potential of adaptive sparse networks as a promising direction for developing efficient and reliable deep learning models.

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