https://github.com/cheind/score-matching
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (5.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: cheind
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 230 KB
Statistics
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
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
Owner
- Name: Christoph Heindl
- Login: cheind
- Kind: user
- Location: Austrian area
- Website: https://cheind.github.io/
- Repositories: 88
- Profile: https://github.com/cheind
I am a computer scientist working at the interface of perception, robotics and deep learning.
GitHub Events
Total
- Watch event: 3
Last Year
- Watch event: 3
Committers
Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Christoph.Heindl | c****d@p****t | 24 |
| Christoph Heindl | c****l@g****m | 17 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: over 1 year ago
All Time
- Total issues: 1
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- cheind (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- matplotlib *
- numpy *
- torch >=1.9.0