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awesome-ebm

全面汇总能量函数模型与学习资源

能量模型机器学习生成模型对比散度神经网络 Github 开源项目

awesome-ebm项目汇集了能量函数模型(EBM)和能量函数学习的全面资源。包括早期至最新的相关论文、教程、讲座和开源库。涵盖EBM在图像生成、分类、密度估计、强化学习等领域的应用，为研究和开发人员提供丰富的学习参考。

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介绍相关项目

Awesome Energy Based Models/Learning (Awesome-EBM)

A comprehensive list of energy based learning papers and materials.

Table of Contents

Workshops & Symposiums
Representative Applications
Papers (Reverse Chronological Order))
- 2022
- 2021
- 2020
- 2019
- 2017 ~ 2018
- 2013 ~ 2016
- 2007 ~ 2012
- Early papers (Before 2007)
Tutorials & Talks & Blogs
Open Source Libraries

Workshops & Symposiums

EBM Workshop at ICLR 2021.

Representative Applications

Data (image/graph/sequence/etc) generation
Discriminative learning: Classification/regression
Density estimation
Maximum entropy reinforcement learning
Out-of-distribution detection (OOD)/anomaly detection/fraud detection
Model calibration
Adversarial robustness
Image inpainting/denoising/super-resolution
Prior modeling
Model-based planning for robotics
Language/Speech modeling

Papers (Reverse Chronological Order)

2023

Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan. (2023).
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. In NeurIPS 2023.
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber. (2023).
Moment Matching Denoising Gibbs Sampling. In NeurIPS 2023.
Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola. (2023).
Compositional Sculpting of Iterative Generative Processes. In NeurIPS 2023.
P Yu, Y Zhu, S Xie, X Ma, R Gao, SC Zhu, YN Wu. (2023).
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. In NeurIPS 2023.
Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan. (2023).
Training Discrete Energy-Based Models with Energy Discrepancy. In ICML SODS Workshop.
W Jiang, J Qin, L Wu, C Chen, T Yang, L Zhang. (2023).
Learning Unnormalized Statistical Models via Compositional Optimization. In ICML 2023.
Aaron Lou, Chenlin Meng, Stefano Ermon. (2023).
Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution. arXiv preprint arXiv:2310.16834.

2022

Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon. (2022).
Concrete Score Matching: Generalized Score Matching for Discrete Data. In NeurIPS 2022.
Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu. (2022).
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. In NeurIPS 2022.
Lianhui Qin, Sean Welleck, Daniel Khashabi, Yejin Choi. (2022).
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics. In NeurIPS 2022.
Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang (2022).
End-to-end Stochastic Programming with Energy-based Model. In NeurIPS 2022.
Xiao, Z., & Han, T. (2022).
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model. In NeurIPS 2022.
Minsoo Kang, Hyewon Yoo, Eunhee Kang, Sehwan Ki, Hyong Euk Lee, Bohyung Han. (2022).
Information-Theoretic Generative Model Compression with Variational Energy-based Model. In NeurIPS 2022.
Blondel, M., Llinares-López, F., Dadashi, R., Hussenot, L., & Geist, M. (2022).
Learning Energy Networks with Generalized Fenchel-Young Losses. In NeurIPS 2022.
Suri, K., Shi, X. Q., Plataniotis, K., & Lawryshyn, Y. (2020).
Energy-based surprise minimization for multi-agent value factorization. In NeurIPS 2022.
Kim, B., & Ye, J. C. (2022).
Energy-Based Contrastive Learning of Visual Representations. In NeurIPS 2022.
Sun, H., Dai, H., Xia, W., & Ramamurthy, A. (2021, September).
Path Auxiliary Proposal for MCMC in Discrete Space. In International Conference on Learning Representations.
Eikema, B., Kruszewski, G., Elsahar, H., & Dymetman, M. (2021).
Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs. arXiv preprint arXiv:2112.05702.
Liu, M., Liu, H., & Ji, S. (2021).
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models.
Zhang, D., Malkin, N., Liu, Z., Volokhova, A., Courville, A., & Bengio, Y. (2022).
Generative Flow Networks for Discrete Probabilistic Modeling. arXiv preprint arXiv:2202.01361.
Zhang, R., Liu, X., & Liu, Q. (2022).
A Langevin-like Sampler for Discrete Distributions. arXiv preprint arXiv:2206.09914.
Xie, B., Yuan, L., Li, S., Liu, C. H., Cheng, X., & Wang, G. (2021).
Active Learning for Domain Adaptation: An Energy-based Approach. AAAI 2022
Sansone, E. (2021).
LSB: Local Self-Balancing MCMC in Discrete Spaces. arXiv preprint arXiv:2109.03867.
Ou, Z., Xu, T., Su, Q., Li, Y., Zhao, P., & Bian, Y. 2022).
Learning Neural Set Functions Under the Optimal Subset Oracle. In NeurIPS 2022 (Oral).

2021

Lazaro-Gredilla, M., Dedieu, A., & George, D. (2021).
Perturb-and-max-product: Sampling and learning in discrete energy-based models. Advances in Neural Information Processing Systems, 34.
Yu, L., Song, J., Song, Y., & Ermon, S. (2021).
Pseudo-Spherical Contrastive Divergence. arXiv preprint arXiv:2111.00780.
Sodhi, P., Dexheimer, E., Mukadam, M., Anderson, S., & Kaess, M. (2021).
LEO: Learning Energy-based Models in Graph Optimization. arXiv preprint arXiv:2108.02274.
Xie, J., Zheng, Z., Fang, X., Zhu, S. C., & Wu, Y. N. (2021).
Cooperative training of fast thinking initializer and slow thinking solver for conditional learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Chen, P. H., Wei, W., Hsieh, C. J., & Dai, B. (2021, July).
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. In International Conference on Machine Learning (pp. 1760-1770). PMLR.
Jaini, P., Holdijk, L., & Welling, M. (2021).
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent. arXiv preprint arXiv:2106.07832.
Grathwohl, W., Swersky, K., Hashemi, M., Duvenaud, D., & Maddison, C. (2021, July).
Oops i took a gradient: Scalable sampling for discrete distributions. In International Conference on Machine Learning (pp. 3831-3841). PMLR.
Yoon, J., Hwang, S. J., & Lee, J. (2021).
Adversarial purification with Score-based generative models. arXiv preprint arXiv:2106.06041.
Domingo-Enrich, C., Bietti, A., Vanden-Eijnden, E., & Bruna, J. (2021).
On Energy-Based Models with Overparametrized Shallow Neural Networks. arXiv preprint arXiv:2104.07531.
2021: Durkan, Conor, and Yang Song.
On Maximum Likelihood Training of Score-Based Generative Models.
2021: Song, Y., & Kingma, D. P.
How to Train Your Energy-Based Models. arXiv preprint arXiv:2101.03288, 2021.
Shi, C., Luo, S., Xu, M., & Tang, J. (2021).
Learning Gradient Fields for Molecular Conformation Generation. arXiv preprint arXiv:2105.03902.
Yang, X., & Ji, S. (2021).
JEM++: Improved Techniques for Training JEM. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6494-6503).
Zhu, Y., Ma, J., Sun, J., Chen, Z., Jiang, R., Chen, Y., & Li, Z. (2021).
Towards understanding the generative capability of adversarially robust classifiers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 7728-7737).
Yang, X., Ye, H., Ye, Y., Li, X., & Ji, S. (2021).
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More. arXiv preprint arXiv:2101.00122.
Suhail, M., Mittal, A., Siddiquie, B., Broaddus, C., Eledath, J., Medioni, G., & Sigal, L. (2021).
Energy-Based Learning for Scene Graph Generation. arXiv preprint arXiv:2103.02221.[code]
Liu, M., Yan, K., Oztekin, B., & Ji, S. (2021).
GraphEBM: Molecular Graph Generation with Energy-Based Models. arXiv preprint arXiv:2102.00546.
Wu, H., Esmaeili, B., Wick, M., Tristan, J. B., & van de Meent, J. W. (2021).
Conjugate Energy-Based Models.
Zheng, Z., Xie, J., & Li, P. (2021).
Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning.
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.
Bian, Y., Rong, Y., Xu, T., Wu, J., Krause, A., & Huang, J. (2021).
Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations. arXiv preprint arXiv:2106.02938.

2020

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.
Du, Y., Li, S., & Mordatch, I. (2020).
Compositional visual generation with energy based models. Advances in Neural Information Processing Systems, 33, 6637-6647.
Du, Y., Li, S., Tenenbaum, J., & Mordatch, I. (2020).
Improved contrastive divergence training of energy based models. arXiv preprint arXiv:2012.01316.
[Meng, C., Yu, L.,

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