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Closed-form Continuous-time Models

Closed-form Continuous-time Neural Networks (CfCs) are powerful sequential liquid neural information processing units.

Paper Open Access: https://www.nature.com/articles/s42256-022-00556-7

Arxiv: https://arxiv.org/abs/2106.13898

A Tutorial on Liquid Neural Networks including Liquid CfCs: https://ncps.readthedocs.io/en/latest/quickstart.html

Requirements

  • Python3.6 or newer
  • Tensorflow 2.4 or newer
  • PyTorch 1.8 or newer
  • pytorch-lightning 1.3.0 or newer
  • scikit-learn 0.24.2 or newer

Module description

  • tf_cfc.py Implementation of the CfC (various versions) in Tensorflow 2.x
  • torch_cfc.py Implementation of the CfC (various versions) in PyTorch
  • train_physio.py Trains the CfC models on the Physionet 2012 dataset in PyTorch (code adapted from Rubanova et al. 2019)
  • train_xor.py Trains the CfC models on the XOR dataset in Tensorflow (code adapted from Lechner & Hasani, 2020)
  • train_imdb.py Trains the CfC models on the IMDB dataset in Tensorflow (code adapted from Keras examples website)
  • train_walker.py Trains the CfC models on the Walker2d dataset in Tensorflow (code adapted from Lechner & Hasani, 2020)
  • irregular_sampled_datasets.py Datasets (same splits) from Lechner & Hasani (2020)
  • duv_physionet.py and duv_utils.py Physionet dataset (same split) from Rubanova et al. (2019)

Usage

All training scripts except the following three flags

  • no_gate Runs the CfC without the (1-sigmoid) part
  • minimal Runs the CfC direct solution
  • use_ltc Runs an LTC with a semi-implicit ODE solver instead of a CfC
  • use_mixed Mixes the CfC's RNN-state with a LSTM to avoid vanishing gradients

If none of these flags are provided, the full CfC model is used

For instance

python3 train_physio.py

train the full CfC model on the Physionet dataset.

Similarly

train_walker.py --minimal

runs the direct CfC solution on the walker2d dataset.

For downloading the Walker2d dataset of Lechner & Hasani 2020, run

source download_dataset.sh

Cite

@article{hasani_closed-form_2022,
	title = {Closed-form continuous-time neural networks},
	journal = {Nature Machine Intelligence},
	author = {Hasani, Ramin and Lechner, Mathias and Amini, Alexander and Liebenwein, Lucas and Ray, Aaron and Tschaikowski, Max and Teschl, Gerald and Rus, Daniela},
	issn = {2522-5839},
	month = nov,
	year = {2022},
}