Architecture design and optimization are challenging problems in the field of artificial neural networks. Working in this context, we here present SPARCS (SPectral ARchiteCture Search), a novel architecture search protocol which exploits the spectral attributes of the inter-layer transfer matrices. SPARCS allows one to explore the space of possible architectures by spanning continuous and differentiable manifolds, thus enabling for gradient-based optimization algorithms to be eventually employed. With reference to simple benchmark models, we show that the newly proposed method yields a self-emerging architecture with a minimal degree of expressivity to handle the task under investigation and with a reduced parameter count as compared to other viable alternatives.
@article{arxiv.2504.00885,
title = {Spectral Architecture Search for Neural Network Models},
author = {Gianluca Peri and Lorenzo Chicchi and Duccio Fanelli and Lorenzo Giambagli},
journal= {arXiv preprint arXiv:2504.00885},
year = {2025}
}