HAMUX built using equinox, minimal implementation. A temporary solution as HAMUX is being rebuilt. See the original HAMUX documentation for explanation.

Consists of 1 main file bbhamux.py (<200 lines of important code) and 1 demo notebook: demo.ipynb

All other files are scaffolding for e.g., docs, tests, pypi.

The documentation is designed to be a thorough but gentle introduction to everything you need to know about (energy-based) Associative Memories and Hopfield Networks.

bbhamux is pronounced as "barebones hamux" or "baby hamux".

pip install bbhamux

All logic is in one file: bbhamux.py. Copy this file into your project, modify as needed. The best kind of research code.

You will need to manually install dependencies:

pip install equinox jax

Install correct version of jaxlib for your hardware (e.g., to run on GPUs).

Run demo.ipynb for an example training on MNIST. Works best with GPU

pip install pytest
pytest test.py

Development

pip install nbdev
# Edit documentation in `nbs/`
nbdev_preview

Deploying

nbdev_docs
# git commit & push `docs/` folder

We use poetry to manage dependencies. Install all dependencies (including nbdev and pytest) with:

poetry install --with docs --with test

If this repository is useful for this work, please cite the following:

@inproceedings{
hoover2022universal,
title={A Universal Abstraction for Hierarchical Hopfield Networks},
author={Benjamin Hoover and Duen Horng Chau and Hendrik Strobelt and Dmitry Krotov},
booktitle={The Symbiosis of Deep Learning and Differential Equations II},
year={2022},
url={https://openreview.net/forum?id=SAv3nhzNWhw}
}