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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, 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
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
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},
}