3 papers
math.CT2026
Presenting Neural Networks via Coherent Functors
Matthew Pugh, Jo Grundy, Corina Cirstea +1
This paper develops a methodology for representing machine learning models as models of formal theories, grounded in the perspective that machine learning models are a form of data…
math.CT2025
Learning Is a Kan Extension
Matthew Pugh, Jo Grundy, Corina Cirstea +1
Previous work has demonstrated that efficient algorithms exist for computing Kan extensions and that some Kan extensions have interesting similarities to various machine learning a…
cs.LG2025
Using Enriched Category Theory to Construct the Nearest Neighbour Classification Algorithm
Matthew Pugh, Jo Grundy, Corina Cirstea +1
This paper is the first to construct and motivate a Machine Learning algorithm solely with Enriched Category Theory, supplementing evidence that Category Theory can provide valuabl…