Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jxzhangjhu/awesome-energy-based-model
Energy-based model
https://github.com/jxzhangjhu/awesome-energy-based-model
List: awesome-energy-based-model
Last synced: 16 days ago
JSON representation
Energy-based model
- Host: GitHub
- URL: https://github.com/jxzhangjhu/awesome-energy-based-model
- Owner: jxzhangjhu
- License: mit
- Created: 2021-02-25T03:26:34.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-02T18:34:03.000Z (almost 4 years ago)
- Last Synced: 2024-10-30T06:57:14.441Z (about 2 months ago)
- Size: 8.79 KB
- Stars: 4
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: license
Awesome Lists containing this project
- ultimate-awesome - awesome-energy-based-model - Energy-based model . (Other Lists / Monkey C Lists)
README
## 📝 Publications
1. ICLR 2021 - [Generalized Energy Based Models](https://arxiv.org/abs/2003.05033) by Michael Arbel et. al.
>[Github Code](https://github.com/MichaelArbel/GeneralizedEBM)
2. ICLR 2021 - [No MCMC for me: Amortized sampling for fast and stable training of energy-based models](https://arxiv.org/abs/2010.04230) by Will Grathwohl et al.
>[Github Code](https://github.com/wgrathwohl/VERA)
3. NeurIPS 2019 - [Implicit Generation and Modeling with Energy-Based Models](https://arxiv.org/abs/1903.08689) by YiLun Du and Igor Mordatch
>[Github Code](https://github.com/openai/ebm_code_release)
4. ICLR 2020 - [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://arxiv.org/abs/1912.03263) by Will Grathwohl et al.
>[Github Code](https://github.com/wgrathwohl/JEM)
5. ICML 2020 - [Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling](https://arxiv.org/abs/2002.05616) by Will Grathwohl et al.
>[Github Code](https://github.com/wgrathwohl/LSD)
6. CVPR 2020 - [Flow Contrastive Estimation of Energy-Based Models](https://openaccess.thecvf.com/content_CVPR_2020/papers/Gao_Flow_Contrastive_Estimation_of_Energy-Based_Models_CVPR_2020_paper.pdf) by Ruiqi Gao et al.
>[Github Code: not available]
7. ICLR 2021 - [Learning Energy-Based Models by Diffusion Recovery Likelihood](http://www.stat.ucla.edu/~ruiqigao/drl/paper.pdf) by Ruiqi Gao et al.
>[Github Code: not available]
8. Arxiv 2021 - [How to Train Your Energy-Based Models](https://arxiv.org/abs/2101.03288) by
>[Github Code: not available]
9. ICLR 2021 - [VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models](https://arxiv.org/abs/2010.00654)
>[Github Code: not available]
10.