https://github.com/avik-pal/regneuralde.jl

Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)

https://github.com/avik-pal/regneuralde.jl

Science Score: 10.0%

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Keywords

differential-equations julia mnist-classification neural-networks neural-ode physionet regularization sciml
Last synced: 5 months ago · JSON representation

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Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)

Basic Info
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  • Stars: 26
  • Watchers: 5
  • Forks: 4
  • Open Issues: 3
  • Releases: 0
Topics
differential-equations julia mnist-classification neural-networks neural-ode physionet regularization sciml
Created over 5 years ago · Last pushed over 3 years ago
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Readme License

README.md

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:

julia ] 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

```bibtex @InProceedings{pmlr-v139-pal21a, title = {Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics}, author = {Pal, Avik and Ma, Yingbo and Shah, Viral and Rackauckas, Christopher V}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8325--8335}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/pal21a/pal21a.pdf}, url = {http://proceedings.mlr.press/v139/pal21a.html}, }

```

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:

bash $ 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

bash $ julia --project=. experiments/latent_ode.jl

TOY NEURAL SDE

To train a Vanilla and Regularized Neural SDE

bash $ 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:

bash $ julia --project=. experiments/mnist_nsde.jl

Owner

  • Name: Avik Pal
  • Login: avik-pal
  • Kind: user
  • Location: Cambridge, MA
  • Company: Massachusetts Institute of Technology

PhD Student @mit || Prev: BTech CSE IITK

GitHub Events

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Last synced: about 2 years ago

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  • Total Commits: 106
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  • Avg Commits per committer: 53.0
  • Development Distribution Score (DDS): 0.009
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Top Committers
Name Email Commits
Avik Pal a****l@i****n 105
Christopher Rackauckas a****s@c****m 1
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Last synced: 9 months ago

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  • Total issues: 8
  • Total pull requests: 90
  • Average time to close issues: 6 months
  • Average time to close pull requests: about 2 months
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  • Total pull request authors: 1
  • Average comments per issue: 2.25
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  • Bot pull requests: 90
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Top Authors
Issue Authors
  • ChrisRackauckas (3)
  • avik-pal (2)
  • jessebett (1)
  • linguo4 (1)
  • EyalRozenberg1 (1)
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  • github-actions[bot] (90)
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