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PreprintCC BY 4.0arXiv

Transversal Pooling Neural Networks

  • Emily J. King
  • Dustin G. Mixon
  • Michael Perlmutter
  • Lander Ver Hoef
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Abstract

Many learning tasks require stability to small transformations while retaining sensitivity to larger ones. We introduce \emph{transversal pooling neural networks}, which generalize spatial max pooling to affine group actions. We establish equivariance to a chosen subgroup and derive explicit stability bounds for individual pooled wavelet coefficients under affine perturbations of the input. Experimentally, we demonstrate the utility of our networks in low-data environments and for predicting tropical cyclone intensification.

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Cite

BibTeXRIS
BibTeX
@misc{king2026transversal,
  title = {{Transversal Pooling Neural Networks}},
  author = {Emily J. King and Dustin G. Mixon and Michael Perlmutter and Lander Ver Hoef},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.36237},
  eprint = {2609.36237},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.36237},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Transversal Pooling Neural Networks
AU  - Emily J. King
AU  - Dustin G. Mixon
AU  - Michael Perlmutter
AU  - Lander Ver Hoef
PY  - 2026
DA  - 2026/09/28
PB  - arXiv
DO  - 10.48550/arxiv.2609.36237
UR  - https://arxiv.org/abs/2609.36237
AB  - Many learning tasks require stability to small transformations while retaining sensitivity to larger ones. We introduce \emph{transversal pooling neural networks}, which generalize spatial max pooling to affine group actions. We establish equivariance to a chosen subgroup and derive explicit stability bounds for individual pooled wavelet coefficients under affine perturbations of the input. Experimentally, we demonstrate the utility of our networks in low-data environments and for predicting tropical cyclone intensification.
ER  -