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.,