[55] Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. Maximum entropy generators for energy-based models. arXiv preprint arXiv:1901.08508, 2019.
[56] Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. Efficient gan-based anomaly detection. Proceedings of the 20th IEEE International Conference on Data Mining (ICDM), 2018, 2018.
[57] Tijmen Tieleman. Training restricted boltzmann machines using approximations to the likelihood gradient. In Proceedings of International Conference on Machine Learning (ICML), 2008.
[58] Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu. A theory of generative convnet. In Proceedings of International Conference on Machine Learning (ICML), 2016.
[59] Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689, 2019.
[60] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved contrastive divergence training of energy based models. In Proceedings of International Conference on Machine Learning (ICML), 2021.
[61] Bo Pang, Tian Han, and Ying Nian Wu. Learning latent space energy-based prior model for molecule generation. arXiv preprint arXiv:2010.09351, 2020.
[62] Bo Pang, Tianyang Zhao, Xu Xie, and Ying Nian Wu. Trajectory prediction with latent belief energy-based model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[63] Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, and Ying Nian Wu. Generative voxelnet: learning energy-based models for 3d shape synthesis and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2468-2484, 2020.
[64] Jianwen Xie, Zilong Zheng, and Ping Li. Learning energy-based model with variational autoencoder as amortized sampler. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2021.
[65] Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li. A tale of two flows: cooperative learning of Langevin flow and normalizing flow toward energy-based model. arXiv preprint arXiv:2205.06924, 2022.
[66] Yaxuan Zhu, Jianwen Xie, Yingnian Wu, and Ruiqi Gao. Learning energy-based models by cooperative diffusion recovery likelihood. arXiv preprint arXiv:2309.05153, 2023.
[67] Geoffrey E Hinton. Training products of experts by minimizing contrastive divergence. Neural computation, 14(8):1771-1800, 2002.
[68] Michael Gutmann and Aapo Hyvarinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010.
[69] Ruiqi Gao, Erik Nijkamp, Diederik P Kingma, Zhen Xu, Andrew M Dai, and Ying Nian Wu. Flow contrastive estimation of energy-based models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[70] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. Advances in neural information processing systems, 2020.
[71] JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith, M West, et al. The variational bayesian em algorithm for incomplete data: with application to scoring graphical model structures. Bayesian statistics, 7(453-464):210, 2003.
[72] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.