RegNeuralDE
Official Implementation of the ICML 2021 Paper Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
USAGE
Experiments provided here were developed and tested on Julia v1.5.3. All other package versions are automatically enforced. To install do the following in Julia REPL:
] dev https://github.com/avik-pal/RegNeuralDE.jl
The code will be downloaded in the JULIA_PKG_DEVDIR
directory.
CITATION
If you found this codebase useful in your research, please consider citing
@inproceedings{
pal2021opening,
title={{O}pening the {B}lackbox: {A}ccelerating {N}eural {D}ifferential {E}quations by {R}egularizing {I}nternal {S}olver {H}euristics},
author={Avik Pal and Yingbo Ma and Viral B. Shah and Christopher Rackauckas},
booktitle={International Conference on Machine Learning},
year={2021},
eprint={2105.03918},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
DATASETS
- Preprocessed Physionet Data can be downloaded from here. Place the downloaded file in
data/physionet.bson
.
EXPERIMENTS
Important Parameters of the Experiments are controlled using the yml
files in experiments/configs
.
SUPERVISED CLASSIFICATION USING NEURAL ODE
Parameters controlled by experiments/configs/mnist_node.yml
. To train a Vanilla/Regularized Neural ODE for MNIST classification:
$ julia --project=. experiments/mnist_node.jl
LATENT ODE FOR TIME SERIES INTERPOLATION
Parameters controlled by experiments/configs/latent_ode.yml
. To train a Vanilla/Regularized Latent ODE with GRU Encoder for Physionet Time Series Interpolation
$ julia --project=. experiments/latent_ode.jl
TOY NEURAL SDE
To train a Vanilla and Regularized Neural SDE
$ julia --project=. experiments/sde_toy_problem.jl
SUPERVISED CLASSIFICATION USING NEURAL SDE
Parameters controlled by experiments/configs/mnist_nsde.yml
. To train a Vanilla/Regularized Neural ODE for MNIST classification:
$ julia --project=. experiments/mnist_nsde.jl