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ZENODO
Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Continuity, Piecewise Corrections, and Functor Models in Function-Based Learning

descriptionPublicationkeyboard_double_arrow_right Article 04 Jan 2026Publisher:Zenodo
Authors: Harby, John;

doi: 10.5281/zenodo.18142519 , 10.5281/zenodo.18142518

Continuity, Piecewise Corrections, and Functor Models in Function-Based Learning

Abstract

This note provides a conceptual and mathematical clarification of continuity, piecewise behavior, and interface-level regularity assumptions in Function Models. It extends and complements the Function Model Architecture described in DOI: 10.5281/zenodo.17786610, with a focus on functorial structure preservation rather than implementation details.

Keywords

Machine Learning, Functor, Artificial intelligence, Artificial Intelligence, Machine learning, Function Model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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