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Neural Tangent Kernels and Fisher Information Matrices for Simple ReLU Networks with Random Hidden Weights

arXiv 2507.18555 ↗First posted 24 Jul 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

Fisher information matrices and neural tangent kernels (NTK) for 2-layer ReLU networks with random hidden weight are argued. We discuss the relation between both notions as a linear transformation and show that spectral decomposition of NTK with concrete forms of eigenfunctions with major eigenvalues. We also obtain an approximation formula of the functions presented by the 2-layer neural networks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 5 references that are in the map. First posted on arXiv 24 Jul 2025.

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References · 4

  1. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  2. 2019On the Inductive Bias of Neural Tangent Kernelscited by 177
  3. 2021Approximate Spectral Decomposition of Fisher Information Matrix for Simple ReLU Networkscited by 2
  4. 2025Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Informationcited by 1

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