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https://github.com/jxzhangjhu/awesome-energy-based-model

Energy-based model
https://github.com/jxzhangjhu/awesome-energy-based-model

List: awesome-energy-based-model

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Energy-based model

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## 📝 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.