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https://github.com/brain-research/l2hmc
TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
https://github.com/brain-research/l2hmc
Last synced: 11 days ago
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TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
- Host: GitHub
- URL: https://github.com/brain-research/l2hmc
- Owner: brain-research
- License: apache-2.0
- Archived: true
- Created: 2017-09-15T20:19:18.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-14T05:29:04.000Z (about 6 years ago)
- Last Synced: 2024-08-01T16:48:12.709Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 13.9 MB
- Stars: 182
- Watchers: 21
- Forks: 40
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# L2HMC: Automatic Training of MCMC Samplers
TensorFlow open source implementation for training MCMC samplers from the paper:
[*Generalizing Hamiltonian Monte Carlo with Neural Networks*](https://arxiv.org/abs/1711.09268)
by [Daniel Levy](http://ai.stanford.edu/~danilevy), [Matt D. Hoffman](http://matthewdhoffman.com/) and [Jascha Sohl-Dickstein](sohldickstein.com)
---
Given an analytically described distributions (implemented as in `utils/distributions.py`), L2HMC enables training of fast-mixing samplers. We provide an example, in the case of the Strongly-Correlated Gaussian, in the notebook `SCGExperiment.ipynb` --other details are included in the paper.
## Contact
***Code author:*** Daniel Levy
***Pull requests and issues:*** @daniellevy
## Citation
If you use this code, please cite our paper:
```
@article{levy2017generalizing,
title={Generalizing Hamiltonian Monte Carlo with Neural Networks},
author={Levy, Daniel and Hoffman, Matthew D. and Sohl-Dickstein, Jascha},
journal={International Conference on Learning Representations},
year={2018}
}
```## Note
This is not an official Google product.