# bgavran/Category_Theory_Machine_Learning

List of papers studying machine learning through the lens of category theory

Repository: https://github.com/bgavran/Category_Theory_Machine_Learning
Canonical: https://ross.abutalabs.com/products/category_theory_machine_learning
Language: Python
License Family: other
Topics: neural-networks, lenses, machine-learning, category-theory
Last push: 2026-07-29T08:15:24+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 35, longevity 100
- inputs: {"age_days": 2246, "days_push": 35, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1538, forks 103 (observed 2026-08-28T04:05:00.252375+00:00)

## What it is
A curated list of papers applying category theory to machine learning, grouped by subfield such as deep learning, probabilistic modeling, and NLP. It serves as a research reference and reading guide rather than a software tool.

## Use cases
- find papers on category theory applied to machine learning
- learn categorical foundations of deep learning
- research backpropagation as a functor
- find theses on categorical deep learning
- explore lenses and learners literature
- survey compositionality in neural networks

## When to choose
- you want a comprehensive reading list on category theory and ML
- you are a researcher exploring categorical approaches to deep learning
- you need references for a paper or thesis on this topic

## When to avoid
- you need working code or a library
- you want an introductory tutorial rather than paper links
- you need practical ML tooling

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation
- domain: machine-learning, artificial-intelligence, tutorials, awesome-lists
- platform: cross-platform
- tags: category-theory, curated-list, papers, research, deep-learning, lenses

## Member repositories
- bgavran/Category_Theory_Machine_Learning (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.252375+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:56.975829+00:00, confidence not recorded.
  - readme: https://github.com/bgavran/Category_Theory_Machine_Learning (fetched 2026-08-28T04:05:00.252375+00:00, sha 75bfba0645e6)
- Data as of 2026-08-30T08:39:29.467469+00:00.