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News/A renormalization-group inspired lattice-based framework for piecewise generalized linear models
arxiv
PublishedMay 8, 2026 at 4:00 AM
—neutral

A renormalization-group inspired lattice-based framework for piecewise generalized linear models

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.05493v1 Announce Type: cross Abstract: We formally introduce a class of models inspired by renormalization group (RG) theory, built on additive hierarchical expansions analogous to those appearing in functional ANOVA and mixed-effects models. Like ReLU convolutional neural networks, they

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Discussion
Mentioned models
04
  • 01
    ReLU convolutional neural networks
  • 02
    piecewise GLMs
  • 03
    hierarchical mixed-effects regressions
  • 04
    regression trees with structured parameter sharing
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#interpretable-ai#statistical-physics#machine-learning#model-design

No replies yet. Be first.

Mentioned models
04
  • 01
    ReLU convolutional neural networks
  • 02
    piecewise GLMs
  • 03
    hierarchical mixed-effects regressions
  • 04
    regression trees with structured parameter sharing
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#interpretable-ai#statistical-physics#machine-learning#model-design

Related coverage

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