https://github.com/ermongroup/sliced_score_matching
Code for reproducing results in the sliced score matching paper (UAI 2019)
https://github.com/ermongroup/sliced_score_matching
density-estimation energy-based-model score-estimation score-matching uai2019
Last synced: 5 months ago
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Code for reproducing results in the sliced score matching paper (UAI 2019)
- Host: GitHub
- URL: https://github.com/ermongroup/sliced_score_matching
- Owner: ermongroup
- License: gpl-3.0
- Created: 2019-06-25T21:29:50.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-01-07T01:39:32.000Z (over 5 years ago)
- Last Synced: 2025-03-31T16:09:48.433Z (6 months ago)
- Topics: density-estimation, energy-based-model, score-estimation, score-matching, uai2019
- Language: Python
- Homepage:
- Size: 8.73 MB
- Stars: 145
- Watchers: 3
- Forks: 23
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Sliced Score Matching: A Scalable Approach to Density and Score Estimation
This repo contains a PyTorch implementation for the paper [Sliced Score Matching: A Scalable Approach to Density and Score Estimation](https://arxiv.org/abs/1905.07088), UAI 2019. Sliced score matching is a scalable variant of score matching that can be used to train unnormalized statistical models or estimating the score (derivatives of the log-density function) of data.
## Dependencies
The following are packages needed for running this repo.
- PyTorch==1.0.1
- TensorFlow==1.12.0
- tqdm
- tensorboardX
- Scipy
- PyYAML
## Running the experiments
```bash
python main.py --runner [runner name] --config [config file]
```
Here `runner name` is one of the following:
- `DKEFRunner`. This corresponds to experiments on deep kernel exponential families.
- `NICERunner`. This corresponds to the sanity check experiment of training a NICE model.
- `VAERunner`. Experiments on VAEs.
- `WAERunner`. Experiments on Wasserstein Auto-Encoders (WAEs).
and `config file` is the directory of some YAML file in `configs/`.
For example, if you want to train an implicit VAE of latent size 8 on MNIST with Sliced Score Matching, just run
```bash
python main.py --runner VAERunner --config vae/mnist_ssm_8.yml
```
## References
If you find the idea or code useful for your research, please consider citing our paper:
```
@inproceedings{song2019sliced,
author = {Yang Song and
Sahaj Garg and
Jiaxin Shi and
Stefano Ermon},
title = {Sliced Score Matching: {A} Scalable Approach to Density and Score
Estimation},
booktitle = {Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial
Intelligence, {UAI} 2019, Tel Aviv, Israel, July 22-25, 2019},
pages = {204},
year = {2019},
url = {http://auai.org/uai2019/proceedings/papers/204.pdf},
}
```