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### 2022

- [ ] [ Xie, B., Yuan, L., Li, S., Liu, C. H., Cheng, X., & Wang, G. (2021). \

- [ ] [Xie, J., Zhu, Y., Li, J., Li, P. 2022\
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model (ICLR) 2022](https://openreview.net/forum?id=31d5RLCUuXC)

- [ ] [ Xie, B., Yuan, L., Li, S., Liu, C. H., Cheng, X., & Wang, G. (2022). \
Active Learning for Domain Adaptation: An Energy-based Approach. AAAI 2022](https://arxiv.org/abs/2112.01406)

- [ ] [Zhang, J., Xie, J., Zheng, Z., Barnes, N. 2022\
Energy-Based Generative Cooperative Saliency Prediction (AAAI 2022)](https://arxiv.org/pdf/2106.13389.pdf)


### 2021



- [ ] [Zhang, J., Xie, J., Barnes, N., Li, P. (2021)\
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction (NeurIPS) 2021](https://arxiv.org/pdf/2112.13528.pdf)

- [ ] [Zhao, Y., Xie, J., Li, P. (2021) \
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling (ICLR 2021)](https://openreview.net/pdf?id=aD1_5zowqV)

- [ ] [Xie, J., Zheng, Z., Fang, X., Zhu, S., Wu, Y. (2021)\
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation (AAAI 2021)](https://arxiv.org/abs/2103.04285)

- [ ] [Xie, J., Zheng, Z., Li, P. (2021)\.
Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler. (AAAI 2021)](https://arxiv.org/pdf/2012.14936.pdf)

- [ ] [Xu, Y., Xie, J., Zhao, T., Baker, C., Zhao, Y., Wu, Y. (2022)\
Energy-Based Continuous Inverse Optimal Control. 2022](https://arxiv.org/pdf/1904.05453.pdf)


- [ ] [Yu, L., Song, J., Song, Y., & Ermon, S. (2021). \
Pseudo-Spherical Contrastive Divergence. arXiv preprint arXiv:2111.00780.](https://arxiv.org/abs/2111.00780)

Expand Down Expand Up @@ -100,7 +125,11 @@ GraphEBM: Molecular Graph Generation with Energy-Based Models. arXiv preprint ar
Conjugate Energy-Based Models.](https://openreview.net/pdf?id=4k58RmAD02)

- [ ] [Zheng, Z., Xie, J., & Li, P. (2021). \
Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning.](http://www.stat.ucla.edu/~jxie/personalpage_file/publications/internal_EBM.pdf)
Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning. CVPR 2021](http://www.stat.ucla.edu/~jxie/personalpage_file/publications/internal_EBM.pdf)

- [ ] [Xie, J., Xu, Y., Zheng, Z., Zhu, S., Wu, Y. (2021)\
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification. CVPR 2021]
(https://arxiv.org/pdf/2004.01301.pdf)

- [ ] [Wu, Jiaxiang and Shen, Tao and Lan, Haidong and Bian, Yatao and Huang, Junzhou. (2021) \
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding.](https://www.biorxiv.org/content/10.1101/2021.06.06.447297)
Expand All @@ -111,6 +140,9 @@ SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding.](https://w

### 2020

- [ ] [Xie, J., Zheng, Z., Gao, R., Wang, W., Zhu, S., Wu, Y. (2020).\
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. PAMI 2020](https://arxiv.org/pdf/2012.13522.pdf)

- [ ] [Grathwohl, W., Wang, K. C., Jacobsen, J. H., Duvenaud, D., & Zemel, R. (2020, November). \
Learning the stein discrepancy for training and evaluating energy-based models without sampling. In International Conference on Machine Learning (pp. 3732-3747). PMLR.](http://proceedings.mlr.press/v119/grathwohl20a.html)

Expand Down Expand Up @@ -221,6 +253,10 @@ From Sets to Multisets: Provable Variational Inference for Probabilistic Integer

### 2019

- [ ] [Xie, J., Zhu, S., Wu, Y. (2019).\
Learning Energy-based Spatial-Temporal Generative ConvNet for Dynamic Patterns. (PAMI 2019)](https://arxiv.org/abs/1909.11975)


- [ ] [Saremi, S., & Hyvärinen, A. (2019).\
Neural empirical bayes. Journal of Machine Learning Research, 20, 1-23.](https://www.jmlr.org/papers/volume20/19-216/19-216.pdf)

Expand Down Expand Up @@ -276,12 +312,18 @@ Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a
Adversarial contrastive estimation. arXiv preprint arXiv:1805.03642.](https://arxiv.org/abs/1805.03642)


- [ ] [Xie, J., Zheng, Z., Gao, R., Wang, W., Zhu, S., Wu, Y. (2018).\
Learning Descriptor Networks for 3D Shape Synthesis and Analysis .CVPR 2018](https://arxiv.org/abs/1804.00586)


- [ ] [Scellier, B., & Bengio, Y. (2017).\
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation. Frontiers in computational neuroscience, 11, 24.](https://www.frontiersin.org/articles/10.3389/fncom.2017.00024/full)

- [ ] [Haarnoja, T., Tang, H., Abbeel, P., & Levine, S. (2017, July).\
Reinforcement learning with deep energy-based policies. In International Conference on Machine Learning (pp. 1352-1361). PMLR.](http://proceedings.mlr.press/v70/haarnoja17a.html)

- [ ] [Xie, J., Zhu, S., Wu, Y. (2018) .\
Synthesizing Dynamic Pattern by Spatial-Temporal Generative ConvNet. CVPR 2017](https://arxiv.org/abs/1606.00972)


### 2013 ~ 2016
Expand Down Expand Up @@ -440,6 +482,12 @@ Generative Modeling by Estimating Gradients of the Data Distribution](https://ww
- [ ] [UvA Deep Learning Tutorials Fall 2020. \
Tutorial 8: Deep Energy-Based Generative Models](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial8/Deep_Energy_Models.html)

- [ ] [Jianwen Xie, Ying Nian Wu. \
CVPR 2021 Tutorial on Theory and Application of Energy-Based Generative Models](https://energy-based-models.github.io/)

- [ ] [Jianwen Xie, Ying Nian Wu. \
ICCV 2021 Tutorial on Theory and Application of Energy-Based Generative Models](https://energy-based-models.github.io/iccv2021-tutorial)


## Open Source Libraries

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