mlsanityData to 7 Oct 2026

What is Smoothness?

arXiv 2609.03246 ↗First posted 3 Sep 2026math.RT cs.LGcited by 0

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Abstract (arXiv)

Smoothness of a function on the real line is reflected in the decay of its Fourier transform, which suggests that smoothness of a function in L2(G) for a group G should mean concentration of the Fourier coefficients at low frequency. Such a reading presupposes an ordering of the irreducible representations of G, but for non-abelian G, no ordering is canonical. Given a symmetric generating set S, the Laplacian of the associated Cayley graph is block diagonal over the dual, and we order the irreps by the mean of the eigenvalues in each block. This produces an ordering function ω:widehatG→ℝ that depends only on the pair (G,S). This function is bounded between zero and two, vanishing only at the trivial representation and achieving the upper bound exactly when the Cayley graph is bipartite. We then ask how much freedom the construction has. Within the class of operators satisfying natural axioms, the induced orderings are exactly the real functions on the dual vanishing at the trivial representation and agreeing on conjugate pairs, and the orderings coming from inversion orbits of conjugacy classes form a basis for them. We cut the freedom down further by requiring two additional inputs: nonnegativity of the class weights and a declaration of which group elements count as uniform incremental changes, which pins the operator to the Cayley-Laplacian up to positive scale. We observe that the construction persists for compact groups even though the Cayley graph does not, and we extend the theory to finite sets carrying a transitive group action, where the acting group selects which frequencies exist and the generating set orders them. The answer to the title question is therefore that smoothness is a property of a function together with a choice of group and generating set, not of the function alone.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 46 references that are in the map. First posted on arXiv 3 Sep 2026.

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16 of 46 references.

References · 16

  1. 2018On the Spectral Bias of Neural Networkscited by 1,321
  2. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  3. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  4. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  5. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  6. 2019Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networkscited by 360
  7. 2019Interpretable machine learning: definitions, methods, and applicationscited by 303
  8. 2020Graph signal processing for machine learning: A review and new perspectivescited by 73
  9. 2022Exploiting symmetry in variational quantum machine learningcited by 68
  10. 2021Geometric Deep Learning and Equivariant Neural Networkscited by 64
  11. 2022Group-Invariant Quantum Machine Learningcited by 56
  12. 2023Graph Signal Processing: History, Development, Impact, and Outlookcited by 39
4 more
  1. 2022Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact casecited by 25
  2. 2026Spectral methods: crucial for machine learning, natural for quantum computers?cited by 3
  3. 2021Signal Processing on the Permutahedron: Tight Spectral Frames for Ranked Data Analysiscited by 2
  4. 2023Frames for signal processing on Cayley graphscited by 1

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