Spectral Architecture Search for Neural Networks

• URL: http://arxiv.org/abs/2504.00885v1
• Date: Tue, 01 Apr 2025 15:14:30 GMT
• Title: Spectral Architecture Search for Neural Networks
• Authors: Gianluca Peri, Lorenzo Giambagli, Lorenzo Chicchi, Duccio Fanelli,
• Abstract summary: We present a novel architecture search protocol which exploits the spectral attributes of the inter-layer transfer matrices.<n>We show that the newly proposed method yields a self-emerging architecture with a minimal degree of expressivity to handle the task under investigation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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