Categorical Foundations of Gradient-Based Learning

2 March 2021

Geoffrey S. H. Cruttwell

Papers citing "Categorical Foundations of Gradient-Based Learning"

29 / 29 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 29 0 0 14 May 2025
Logic Explanation of AI Classifiers by Categorical Explaining Functors S. Fioravanti Francesco Giannini Paolo Frazzetto Fabio Zanasi Pietro Barbiero 54 0 0 20 Mar 2025
Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning Georgios Bakirtzis M. Savvas Ruihan Zhao Sandeep P. Chinchali Ufuk Topcu 36 2 0 23 Aug 2024
On the Anatomy of Attention Nikhil Khatri Tuomas Laakkonen Jonathon Liu Vincent Wang-Ma'scianica 3DV 48 1 0 02 Jul 2024
A Pattern Language for Machine Learning Tasks Benjamin Rodatz Ian Fan Tuomas Laakkonen Neil John Ortega Thomas Hoffman Vincent Wang-Ma'scianica 56 3 0 02 Jul 2024
Reinforcement Learning in Categorical Cybernetics Jules Hedges Riu Rodríguez Sakamoto 40 3 0 03 Apr 2024
Deep Learning with Parametric Lenses Geoffrey S. H. Cruttwell Bruno Gavranović Neil Ghani Paul W. Wilson Fabio Zanasi 33 2 0 30 Mar 2024
Generalized Gradient Descent is a Hypergraph Functor Tyler Hanks Matthew Klawonn James Fairbanks 27 0 0 28 Mar 2024
The Topos of Transformer Networks Mattia Jacopo Villani Peter McBurney 37 0 0 27 Mar 2024
Pooling Image Datasets With Multiple Covariate Shift and Imbalance Sotirios Panagiotis Chytas Vishnu Suresh Lokhande Peiran Li Vikas Singh OOD 38 0 0 05 Mar 2024
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures Bruno Gavranovic Paul Lessard Andrew Dudzik Tamara von Glehn J. G. Araújo Petar Velickovic 40 8 0 23 Feb 2024
Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures Vincent Abbott 40 5 0 08 Feb 2024
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics Yivan Zhang Masashi Sugiyama 22 1 0 19 May 2023
The Compositional Structure of Bayesian Inference Dylan Braithwaite Jules Hedges T. S. C. Smithe 22 7 0 10 May 2023
Categorical Foundations of Explainable AI: A Unifying Theory Pietro Barbiero S. Fioravanti Francesco Giannini Alberto Tonda Pietro Lio' Elena Di Lavore XAI 30 2 0 27 Apr 2023
Graph Convolutional Neural Networks as Parametric CoKleisli morphisms Bruno Gavranović M. Villani GNN 86 0 0 01 Dec 2022
Space-time tradeoffs of lenses and optics via higher category theory Bruno Gavranović 16 5 0 19 Sep 2022
Compositional Active Inference II: Polynomial Dynamics. Approximate Inference Doctrines T. S. C. Smithe 30 1 0 25 Aug 2022
Diegetic Representation of Feedback in Open Games Matteo Capucci 20 5 0 24 Jun 2022
A Probabilistic Generative Model of Free Categories Eli Sennesh T. Xu Y. Maruyama 16 0 0 09 May 2022
Categories of Differentiable Polynomial Circuits for Machine Learning Paul W. Wilson Fabio Zanasi 18 6 0 12 Mar 2022
A category theory framework for Bayesian learning Kotaro Kamiya John Welliaveetil BDL 4 2 0 29 Nov 2021
Deep neural networks as nested dynamical systems David I. Spivak Timothy Hosgood 11 1 0 01 Nov 2021
Generalized Optimization: A First Step Towards Category Theoretic Learning Theory Dan Shiebler 32 2 0 20 Sep 2021
Category Theory in Machine Learning Dan Shiebler Bruno Gavranović Paul W. Wilson 21 31 0 13 Jun 2021
Categorical composable cryptography Anne Broadbent M. Karvonen 26 7 0 12 May 2021
Diagrammatic Differentiation for Quantum Machine Learning Alexis Toumi Richie Yeung G. Felice 30 20 0 14 Mar 2021
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits Paul W. Wilson Fabio Zanasi 41 15 0 26 Jan 2021
Categorical Stochastic Processes and Likelihood Dan Shiebler 6 9 0 10 May 2020