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cs.LG2026
Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks
Yoshihiro Maruyama
Symmetry is everywhere in nature and society. Geometric deep learning builds architectures respecting group symmetries, whereas topological deep learning organizes computation thro…
cs.LG2025
Categorical Equivariant Deep Learning: Category-Equivariant Neural Networks and Universal Approximation Theorems
Yoshihiro Maruyama
We develop a theory of category-equivariant neural networks (CENNs) that unifies group/groupoid-equivariant networks, poset/lattice-equivariant networks, graph and sheaf neural net…
cs.LG2023★ 1 cited
Computing with Categories in Machine Learning
Eli Sennesh, Tom Xu, Yoshihiro Maruyama
Category theory has been successfully applied in various domains of science, shedding light on universal principles unifying diverse phenomena and thereby enabling knowledge transf…