Backprop as Functor: A compositional perspective on supervised learning

28 November 2017

Papers citing "Backprop as Functor: A compositional perspective on supervised learning"

12 / 12 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
Deep Learning with Parametric Lenses Geoffrey S. H. Cruttwell Bruno Gavranović Neil Ghani Paul W. Wilson Fabio Zanasi 33 2 0 30 Mar 2024
Active Inference in String Diagrams: A Categorical Account of Predictive Processing and Free Energy Sean Tull Johannes Kleiner T. S. C. Smithe AI4CE 32 0 0 01 Aug 2023
Generalized Optimization: A First Step Towards Category Theoretic Learning Theory Dan Shiebler 32 2 0 20 Sep 2021
Categorical composable cryptography Anne Broadbent M. Karvonen 28 7 0 12 May 2021
Categorical Foundations of Gradient-Based Learning Geoffrey S. H. Cruttwell Bruno Gavranović Neil Ghani Paul W. Wilson Fabio Zanasi FedML 20 70 0 02 Mar 2021
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits Paul W. Wilson Fabio Zanasi 41 15 0 26 Jan 2021
Learning Functors using Gradient Descent Bruno Gavranovic 13 5 0 15 Sep 2020
Smart Choices and the Selection Monad M. Abadi G. Plotkin 14 3 0 17 Jul 2020
Optimizing Neural Networks via Koopman Operator Theory Akshunna S. Dogra William T. Redman 13 49 0 03 Jun 2020
Lenses and Learners Brendan Fong Michael Johnson 11 24 0 05 Mar 2019
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 259 3,239 0 24 Nov 2016