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135-variational-inference-with-gaussian-score-matching

https://github.com/szu-advtech-2024/135-variational-inference-with-gaussian-score-matching

Science Score: 41.0%

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    Links to: arxiv.org
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  • Owner: SZU-AdvTech-2024
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Citation

https://github.com/SZU-AdvTech-2024/135-Variational-Inference-with-Gaussian-Score-Matching/blob/main/ 

Variational Inference (VI) with Gaussian Score Matching (GSM) (NeurIPS 2023)

GSM-VI, which is described in the NeurIPS 2023 paper https://arxiv.org/abs/2307.07849.



GSM-VI fits a multivariate Gasussian distribution with dense covaraince matrix to the target distribution
by score matching. It only requires access to the score function i.e. the gradient of the target log-probability
distribution and implements analytic updates for the variational parameters (mean and covariance matrix).



To install, run

```
pip install numpy==1.26.4
pip install opencv-python==4.10.0.84
pip install opencv-contrib-python==4.10.0.84
pip install jax==0.4.33
```


The example usage code is in `initializers.py`:

```
python3 initializers.py
```

Owner

  • Name: SZU-AdvTech-2024
  • Login: SZU-AdvTech-2024
  • Kind: organization

Citation (citation.txt) 

@article{REPO135,
    author = "Modi, Chirag and Margossian, Charles C. and Yao, Yuling and Gower, Robert Mansel and Blei, David M. and Saul, Lawrence K.",
    journal = "ArXiv",
    title = "{Variational Inference with Gaussian Score Matching}",
    url = "https://api.semanticscholar.org/CorpusID:259936958",
    volume = "abs/2307.07849",
    year = "2023"
}

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