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README.md

RBP

This is the PyTorch implementation of Recurrent Back Propagation as described in the following ICML 2018 paper:

@article{liao2018reviving,
  title={Reviving and Improving Recurrent Back-Propagation},
  author={Liao, Renjie and Xiong, Yuwen and Fetaya, Ethan and Zhang, Lisa and Yoon, KiJung and Pitkow, Xaq and Urtasun, Raquel and Zemel, Richard},
  journal={arXiv preprint arXiv:1803.06396},
  year={2018}
}

Setup

To set up experiments, we need to build our customized operators by running the following scripts:

./setup.sh

Dependencies

Python 3, PyTorch(0.4.0)

Run Demos

  • To run experiments X where X is one of {hopfield, cora, pubmed, hypergrad}:

    python run_exp.py -c config/X.yaml

Notes:

  • Most hyperparameters in the configuration yaml file are self-explanatory.
  • To switch between BPTT, TBPTT and RBP variants, you need to specify grad_method in the config file.
  • Conjugate gradient based RBP requires support of forward mode auto-differentiation which we only provided for the experiments of Hopfield networks and graph neural networks (GNNs). You can check the comments in model/rbp.py for more details.

Cite

Please cite our paper if you use this code in your research work.

Questions/Bugs

Please submit a Github issue or contact rjliao@cs.toronto.edu if you have any questions or find any bugs.

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Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018

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