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Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS

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arxiv 2009.10683 v5 pith:EOGSDLQH submitted 2020-09-22 cs.LG math.STstat.MLstat.TH

classification cs.LGmath.STstat.MLstat.TH
keywords kernelmathbbrkhswhendeeplaplaceneuralpower
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abstract

We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere $\mathbb{S}^{d-1}$. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less smooth) leads to a larger RKHS, when it is restricted to the sphere $\mathbb{S}^{d-1}$ and when it is defined on the entire $\mathbb{R}^d$.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Cost of Discretization in Functional Linear Regression: Minimax Rates and Adaptation

    math.ST 2026-07 accept novelty 7.0 of 10

    Matching minimax prediction rates for discretely observed functional linear regression are n^{-ν/(ν+1)}+(nm)^{-ν/κ} under independent design, and those two terms plus m^{-ν}+m^{-4α} under common design.

  2. Deep Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure

    cs.LG 2025-02 conditional novelty 5.0 of 10

    Adding Fourier features to the input layer lets the Deep Ritz Method generate high-frequency solutions for non-convex multi-well energy problems, while increasing network depth alone does not consistently do so.

  3. Optimal Convergence Rates for Neural Operators

    stat.ML 2024-12 conditional novelty 5.0 of 10

    Two-layer neural operators trained with early-stopped gradient descent achieve the same minimax convergence rates as kernel methods in the neural tangent kernel regime.

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