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Main Authors: Van Nguyen, Bich, Thang, Nguyen Cao Manh
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.08397
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author Van Nguyen, Bich
Thang, Nguyen Cao Manh
author_facet Van Nguyen, Bich
Thang, Nguyen Cao Manh
contents In this paper we prove theorems characterizing the decomposition of equivariant feature spaces, filters and a structural preservation theorem for invariant subspace chains in group equivariant convolutional neural networks(G-CNN). Furthermore, we give explicit matrix forms for irreducible representations of $UT_3(\F_3)$-the unitriangular matrix groups over the field with three elements. These results provide a foundation for designing new G-CNN architectures via representations of $UT_3(\F_3)$ that respect deep algebraic structure, with potential applications in symbolic visual learning.
format Preprint
id arxiv_https___arxiv_org_abs_2507_08397
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Representation Theory of $UT_3(\mathbb{F}_3)$ and its Applications to Equivariant Decomposition in Neural Architectures
Van Nguyen, Bich
Thang, Nguyen Cao Manh
Representation Theory
20C35
In this paper we prove theorems characterizing the decomposition of equivariant feature spaces, filters and a structural preservation theorem for invariant subspace chains in group equivariant convolutional neural networks(G-CNN). Furthermore, we give explicit matrix forms for irreducible representations of $UT_3(\F_3)$-the unitriangular matrix groups over the field with three elements. These results provide a foundation for designing new G-CNN architectures via representations of $UT_3(\F_3)$ that respect deep algebraic structure, with potential applications in symbolic visual learning.
title Representation Theory of $UT_3(\mathbb{F}_3)$ and its Applications to Equivariant Decomposition in Neural Architectures
topic Representation Theory
20C35
url https://arxiv.org/abs/2507.08397