https://github.com/cheind/score-matching

https://github.com/cheind/score-matching

Last synced: 8 months ago
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• Host: GitHub
• URL: https://github.com/cheind/score-matching
• Owner: cheind
• License: mit
• Created: 2021-12-15T07:21:53.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-11-08T06:51:02.000Z (over 3 years ago)
• Last Synced: 2025-01-31T04:09:00.681Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 230 KB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# score-models

The aim of *score-models* is to trace the history and evolution of score-matching models for sampling from a data distribution. 

## References

```bibtex

@article{hyvarinen2005estimation,

  title={Estimation of non-normalized statistical models by score matching.},

  author={Hyv{\"a}rinen, Aapo and Dayan, Peter},

  journal={Journal of Machine Learning Research},

  volume={6},

  number={4},

  year={2005}

}

@inproceedings{song2020sliced,

  title={Sliced score matching: A scalable approach to density and score estimation},

  author={Song, Yang and Garg, Sahaj and Shi, Jiaxin and Ermon, Stefano},

  booktitle={Uncertainty in Artificial Intelligence},

  pages={574--584},

  year={2020},

  organization={PMLR}

}

@article{song2019generative,

  title={Generative modeling by estimating gradients of the data distribution},

  author={Song, Yang and Ermon, Stefano},

  journal={arXiv preprint arXiv:1907.05600},

  year={2019}

}

@article{vincent2011connection,

  title={A connection between score matching and denoising autoencoders},

  author={Vincent, Pascal},

  journal={Neural computation},

  volume={23},

  number={7},

  pages={1661--1674},

  year={2011},

  publisher={MIT Press}

}

```

Additional

https://courses.cs.washington.edu/courses/cse599i/20au/resources/L17_denoising.pdf

How to Train Your Energy-Based Models

https://arxiv.org/pdf/2101.03288.pdf

A Low Rank Approach to Automatic Differentiation

https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.7201&rep=rep1&type=pdf

Sliced Score Matching

https://arxiv.org/pdf/1905.07088.pdf

https://arxiv.org/pdf/2101.09258.pdf