https://github.com/nimadehmamy/L-conv-code

Lie Algebra Convolutional Network implementation
https://github.com/nimadehmamy/L-conv-code

Last synced: 27 days ago
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Lie Algebra Convolutional Network implementation

• Host: GitHub
• URL: https://github.com/nimadehmamy/L-conv-code
• Owner: nimadehmamy
• License: mit
• Created: 2021-10-29T21:53:24.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2021-12-07T08:28:42.000Z (over 2 years ago)
• Last Synced: 2024-02-18T23:37:12.347Z (4 months ago)
• Language: Jupyter Notebook
• Size: 6.46 MB
• Stars: 40
• Watchers: 3
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-stars - nimadehmamy/L-conv-code - Lie Algebra Convolutional Network implementation (Jupyter Notebook)
• awesome-stars - nimadehmamy/L-conv-code - Lie Algebra Convolutional Network implementation (Jupyter Notebook)

README

        
# Lie Algebra Convolutional Network (L-conv) implementation  

__Paper:__ [Automatic Symmetry Discovery with Lie Algebra Convolutional Network](https://papers.nips.cc/paper/2021/file/148148d62be67e0916a833931bd32b26-Paper.pdf) _Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu_ NeurIPS 2021  

(find updated versions on [arxiv](https://arxiv.org/abs/2109.07103)) 

## Contents

A simple implementation of the L-conv layer in PyTorch (>=1.8) can be found in `src/lconv.py`.

The L-conv layer acts similar to a graph convlutional layer (GCN), so prepare your input in a similar fashion (e.g. flatten the spatial dimensions). 

The input should have shape `(batch, channels, #nodes)` (e.g. on an image, # nodes = # pixels)  

This repository also contains code and notebooks for the experiemnts in the paper (appendix C and D) under `paper-code`. 

Most experiments in appendix D use an older (but identical) implementation in Tensoflow (>=2.1). 

Comparison with LieConv in appendix D requires the [LieConv](https://github.com/mfinzi/LieConv) packages. 

### TBA soon:

Exmaples of uses will be added soon.