mlsanityData to 14 Oct 2026

A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance

arXiv 2605.03262 ↗First posted 5 May 2026cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Inverse-multiquadric (IMQ) kernel sections decay at infinity. Can polynomial alignment add persistent directional responses while preserving universal approximation? The yat (Yat) kernel answers this question by multiplying IMQ distance by a biased squared inner product. With shared positive bias and regularization, its RKHS continuously contains the IMQ RKHS and is universal on every compact domain. Alignment strictly enlarges the global function space: Yat sections retain a quadratic directional trace at infinity. We quantify a second distinction through high-dimensional activation covariance. For a fixed number of orthogonal unit centers and fixed kernel parameters, isotropic Gaussian inputs yield a rank-one rescaled limiting covariance for IMQ and a full-rank limit for Yat. On the radius-√ d sphere, both limits are full rank, isolating shared input-norm fluctuations as the Gaussian collapse mechanism. The connection is also constructive: three positive-bias Yat atoms recover one IMQ section exactly, and three are necessary at nonzero centers when regularization is shared and bias varies across atoms. These results identify how polynomial alignment preserves radial approximation while adding directional structure.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 5 May 2026.

Builds on

18 of 48 references.

References · 18

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  3. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  4. 2019Compressive Transformers for Long-Range Sequence Modellingcited by 726
  5. 2017Spectrally-normalized margin bounds for neural networkscited by 686
  6. 2015Deep Kernel Learningcited by 445
  7. 2014Breaking the Curse of Dimensionality with Convex Neural Networkscited by 331
  8. 2017A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networkscited by 314
  9. 2015The Power of Depth for Feedforward Neural Networkscited by 183
  10. 2019Linearized two-layers neural networks in high dimensioncited by 155
  11. 2016Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivitycited by 138
  12. 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 132
6 more
  1. 2018A Priori Estimates of the Population Risk for Two-layer Neural Networkscited by 76
  2. 2020Rational neural networkscited by 75
  3. 2019Pad'e Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networkscited by 67
  4. 2014Convolutional Kernel Networkscited by 47
  5. 2017Neural networks and rational functionscited by 40
  6. 2017A representer theorem for deep kernel learningcited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.