A Brain-inspired Algorithm for Training Highly Sparse Neural Networks

This repository contains code for the paper, "A Brain-inspired Algorithm for Training Highly Sparse Neural Networks" by Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy. For more information please read the paper at https://arxiv.org/abs/1903.07138.

Methodology

Results on highly sparse neural networks

Classification accuracy (%) comparison among methods on a highly large and sparse3-layer MLP with a density lower than 0.22%.

Prerequisites

We run this code on Python 3. Following Python packages have to be installed before executing the project code:

• numpy
• scipy
• sklearn
• Keras

Reference

If you use this code, please consider citing the following paper:

@article{atashgahi2022brain,
  title={A brain-inspired algorithm for training highly sparse neural networks},
  author={Atashgahi, Zahra and Pieterse, Joost and Liu, Shiwei and Mocanu, Decebal Constantin and Veldhuis, Raymond and Pechenizkiy, Mykola},
  journal={Machine Learning},
  pages={1--42},
  year={2022},
  publisher={Springer}
}

Acknowledgements

Starting of the code is "Rigging the Lottery: Making All Tickets Winners" which is available at: https://github.com/google-research/rigl

@inproceedings{evci2020rigging,
  title={Rigging the lottery: Making all tickets winners},
  author={Evci, Utku and Gale, Trevor and Menick, Jacob and Castro, Pablo Samuel and Elsen, Erich},
  booktitle={International Conference on Machine Learning},
  pages={2943--2952},
  year={2020},
  organization={PMLR}
}

Contact

email: z.atashgahi@utwente.nl