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

Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology arXiv:2605.24608v1 Announce Type: cross 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 (MMBB) universal representation theory for translation-invariant operators, which we apply syst