Position: Categorical Deep Learning is an Algebraic Theory of All Architectures

23 February 2024

Papers citing "Position: Categorical Deep Learning is an Algebraic Theory of All Architectures"

8 / 8 papers shown

Title
Accelerating Machine Learning Systems via Category Theory: Applications to Spherical Attention for Gene Regulatory Networks Vincent Abbott Kotaro Kamiya Gerard Glowacki Yu Atsumi Gioele Zardini Yoshihiro Maruyama 26 0 0 14 May 2025
Fundamental Components of Deep Learning: A category-theoretic approach Bruno Gavranović 26 4 0 13 Mar 2024
Categorical semantics of compositional reinforcement learning Georgios Bakirtzis M. Savvas Ufuk Topcu CoGe 40 4 0 29 Aug 2022
Noether Networks: Meta-Learning Useful Conserved Quantities Ferran Alet Dylan D. Doblar Allan Zhou J. Tenenbaum Kenji Kawaguchi Chelsea Finn 70 26 0 06 Dec 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,106 0 27 Apr 2021
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs P. D. Haan Maurice Weiler Taco S. Cohen Max Welling 102 127 0 11 Mar 2020
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li ... J. Zhao Haotong Zhang Alex Smola Jinyang Li Zheng-Wei Zhang AI4CE GNN 197 746 0 03 Sep 2019
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen Mario Geiger Maurice Weiler MLT AI4CE 165 308 0 05 Nov 2018