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News/Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology
arxiv
PublishedMay 26, 2026 at 4:00 AM

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology

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Publisher summary· verbatim

arXiv:2605.24608v1 Announce Type: new Abstract: We develop a rigorous algebraic framework for deep convolutional architectures, CNNs, ResNets, and encoder--decoder networks such as UNet, grounded in lattice theory and mathematical morphology. The central tool is the Matheron--Maragos--Banon--Barrera

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