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This paper advances deep learning theory by investigating higher-arity tensor operations and proposing a hypergraphical generalization of the multilayer perceptron. The study bridges theoretical concepts with empirical analysis, exploring connections to evolutionary algorithms as potential future research directions. It represents a significant step toward understanding complex structural phenomena within trained neural networks beyond standard pairwise interactions.
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Higher Structures in Deep Learning

arXiv1 min read

arXiv:2609.00472v1 Announce Type: new Abstract: We provide an expository introduction on the importance of higher-arity tensor operations to deep learning. Then, we conduct a novel empirical investigation of higher-arity phenomenon in trained neural networks, introduce a hypergraphical generalization of the multilayer perceptron, and explore connections to evolutionary algorithms. We conclude with a discussion of promising directions for future research.

Abstract:We provide an expository introduction on the importance of higher-arity tensor operations to deep learning. Then, we conduct a novel empirical investigation of higher-arity phenomenon in trained neural networks, introduce a hypergraphical generalization of the multilayer perceptron, and explore connections to evolutionary algorithms. We conclude with a discussion of promising directions for future research.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.00472 [cs.LG]

(or arXiv:2609.00472v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.00472 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 151 words

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