arlatentsde 0.2.0

Amortized Reparametrization for Continuous Time Autoencoders (ARCTA)

Amortized reparametrization: Efficient and Scalable Variational Inference for Latent SDEs

Accompanying code for the NeurIPS 2023 paper by Kevin Course and Prasanth B. Nair.

Tutorials and documentation coming soon!

1. Installation

Installing the package

The package can be installed from PyPI:

pip install arlatentsde

Reproducing the experiment environment

We ran experiments on a Linux machine with CUDA 11.8. We used poetry to manage dependencies.

If you prefer a different environment manager, all dependencies are listed in the pyproject.toml.

To reproduce the experiment environment, first navigate to branch named neurips-freeze. Then install all optional dependencies required to run experiments,

poetry install --with dev,exps

To download all pretrained models, datasets, and figures we use repopacker:

repopacker download models-data-figs.zip
repopacker unpack models-data-figs.zip

2. Usage

The numerical studies can be rerun from the experiments directory using the command-line script main.py. All numerical studies follow the same basic structure: (i) generate / download, (ii) train model, and (iii) post process for plots and tables.

The script has the following syntax:

python main.py [experiment] [action]

The choices of experiments and actions are provided below:

• Experiments:
  • predprey: Orders of magnitude magnitude fewer NFEs experiment
  • lorenz: Adjoint instabilities experiment
  • mocap: Motion capture benchmark
  • nsde-video: Neural SDE from video experiment
  • grad-variance: Gradient variance experiment
• Actions:
  • get-data: Download / generate data
  • train: Train models
  • post-process: Post process for plots and tables

3. Reference

Course, K., Nair, P.B. Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs.
In Proc. Advances in Neural Information Processing Systems, (2023).

@inproceedings{
course2023amortized,
title={Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent {SDE}s},
author={Kevin Course and Prasanth B. Nair},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=5yZiP9fZNv}
}