[2605.04325] On the Architectural Complexity of Neural Networks

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Computer Science > Machine Learning

arXiv:2605.04325 (cs)

[Submitted on 5 May 2026]

Title:On the Architectural Complexity of Neural Networks

Authors:Nicholas J. Cooper, François G. Meyer, Michael L. Roberts, Carlos Zapata-Carratalá, Lijun Chen, Danna Gurari<br>View a PDF of the paper titled On the Architectural Complexity of Neural Networks, by Nicholas J. Cooper and 5 other authors

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Abstract:We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addresses a gap in existing theory by explicitly modeling the structure of tensor operations -- lower level information that is often abstracted. Our framework enables two novel objectives: (1) analysis of the evolution of architectural complexity over deep learning history, and (2) automatic construction of novel architectures based on new types of tensor operations. Our study of DNNs introduced over the past 40 years reveals a connection between groundbreaking architectures and increases in different types of architectural complexity. Moreover, we identify several large classes of higher complexity architectures that have not yet been explored. We then collect a dataset of 3,000+ higher complexity architectures, which we publicly release at: this https URL.

Comments:<br>67 pages, 54 figures, 11 tables

Subjects:

Machine Learning (cs.LG); Discrete Mathematics (cs.DM); Combinatorics (math.CO)

Cite as:<br>arXiv:2605.04325 [cs.LG]

(or<br>arXiv:2605.04325v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.04325

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Nicholas Cooper [view email]<br>[v1]<br>Tue, 5 May 2026 22:05:34 UTC (8,638 KB)

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