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Embedding Inequalities for Barron-type Spaces

Lei Wu

TL;DR

The paper establishes a dimension-free embedding between Barron-type function spaces for compact domains: for $s\in\mathbb{N}^+$ and $\delta\in(0,1)$, the inequality $\delta \|f\|_{\mathcal{F}_{s-\delta}(\Omega)} \lesssim_s \|f\|_{\mathcal{B}_s(\Omega)} \lesssim_s \|f\|_{\mathcal{F}_{s+1}(\Omega)}$ holds, linking the Barron and spectral Barron norms with constants independent of the input dimension. The upper bound aligns with previous results, while the main contribution is proving the sharp, dimension-free lower bound via a Fourier-analytic construction of neuron extensions and a measure-based representation of $f$. The results are shown to be tight, with a concrete example establishing the necessity of $\delta>0$, and they suggest that the embedding is robust in high dimensions. These findings unify Barron-type spaces and have potential impact on high-dimensional approximation of functions and PDEs with two-layer networks, motivating extensions to non-integer $s$ and other activations.

Abstract

An important problem in machine learning theory is to understand the approximation and generalization properties of two-layer neural networks in high dimensions. To this end, researchers have introduced the Barron space $\mathcal{B}_s(Ω)$ and the spectral Barron space $\mathcal{F}_s(Ω)$, where the index $s\in [0,\infty)$ indicates the smoothness of functions within these spaces and $Ω\subset\mathbb{R}^d$ denotes the input domain. However, the precise relationship between the two types of Barron spaces remains unclear. In this paper, we establish a continuous embedding between them as implied by the following inequality: for any $δ\in (0,1), s\in \mathbb{N}^{+}$ and $f: Ω\mapsto\mathbb{R}$, it holds that \[ δ\|f\|_{\mathcal{F}_{s-δ}(Ω)}\lesssim_s \|f\|_{\mathcal{B}_s(Ω)}\lesssim_s \|f\|_{\mathcal{F}_{s+1}(Ω)}. \] Importantly, the constants do not depend on the input dimension $d$, suggesting that the embedding is effective in high dimensions. Moreover, we also show that the lower and upper bound are both tight.

Embedding Inequalities for Barron-type Spaces

TL;DR

The paper establishes a dimension-free embedding between Barron-type function spaces for compact domains: for and , the inequality holds, linking the Barron and spectral Barron norms with constants independent of the input dimension. The upper bound aligns with previous results, while the main contribution is proving the sharp, dimension-free lower bound via a Fourier-analytic construction of neuron extensions and a measure-based representation of . The results are shown to be tight, with a concrete example establishing the necessity of , and they suggest that the embedding is robust in high dimensions. These findings unify Barron-type spaces and have potential impact on high-dimensional approximation of functions and PDEs with two-layer networks, motivating extensions to non-integer and other activations.

Abstract

An important problem in machine learning theory is to understand the approximation and generalization properties of two-layer neural networks in high dimensions. To this end, researchers have introduced the Barron space and the spectral Barron space , where the index indicates the smoothness of functions within these spaces and denotes the input domain. However, the precise relationship between the two types of Barron spaces remains unclear. In this paper, we establish a continuous embedding between them as implied by the following inequality: for any and , it holds that Importantly, the constants do not depend on the input dimension , suggesting that the embedding is effective in high dimensions. Moreover, we also show that the lower and upper bound are both tight.
Paper Structure (6 sections, 5 theorems, 36 equations, 1 figure)

This paper contains 6 sections, 5 theorems, 36 equations, 1 figure.

Key Result

Theorem 1.4

Let $\Omega\subset \mathbb{R}^d$ be a compact set. For any $s\in \mathbb{N}^{+}, f\in \mathcal{B}_s(\Omega), \delta\in (0,1)$, we have where $s$ in the upper bound can take the value of $0$.

Figures (1)

  • Figure 1: The triangular function $t(x):=\max(1-|x|,0)$.

Theorems & Definitions (13)

  • Definition 1.1: barron1993universal
  • Definition 1.2
  • Definition 1.3
  • Theorem 1.4
  • Proposition 1.5
  • Lemma 2.1
  • proof
  • Lemma 2.2
  • Remark 2.3
  • proof
  • ...and 3 more