diff --git a/README.md b/README.md index 6c66f3c..6071185 100644 --- a/README.md +++ b/README.md @@ -42,12 +42,37 @@ ### 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) @@ -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) @@ -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) @@ -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) @@ -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 @@ -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