Warming Up Recurrent Neural Networks to Maximise Reachable Multistability Greatly Improves Learning

Official implementation of the warmup initialisation procedure for RNNs.

This repository contains the datasets, recurrent cells and experiments presented the paper. See python supervised.py -h and python reinforcement.py -h for usage details. If you find it useful, please reference in your paper:

@article{lambrechts2023warming,
  title={Warming up recurrent neural networks to maximise reachable multistability greatly improves learning},
  author={Lambrechts, Gaspard and De Geeter, Florent and Vecoven, Nicolas and Ernst, Damien and Drion, Guillaume},
  journal={Neural Networks},
  year={2023},
  publisher={Elsevier}
}

To learn more:

• Blog post
• Research paper