[35] Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. Deep anomaly detection with outlier exposure. In International Conference on Learning Representations, 2019.
[36] Peter W. Higgs. Broken symmetries, massless particles and gauge fields. Phys. Lett., 12:132-133, 1964.
[37] Geoffrey E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Comput., 14(8):1771-1800, aug 2002.
[38] J J Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8):2554-2558, 1982.
[39] Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya, Maurizio Pierini, Kinga A. Wozniak, Jennifer Ngadiuba, Javier Duarte, and Steven Tsan. Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows. Front. Big Data, 5:803685, 2022.
[40] Long Jin, Justin Lazarow, and Zhuowen Tu. Introspective classification with convolutional nets. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
[41] Alan Kahn, Julia Gonski, Inês Ochoa, Daniel Williams, and Gustaaf Brooijmans. Anomalous jet identification via sequence modeling. JINST, 16(08):P08012, 2021.
[42] Raghav Kansal, Javier Duarte, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, and Dimitrios Gunopoulos. Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics. In 34th Conference on Neural Information Processing Systems, 11 2020.
[43] Raghav Kansal, Javier Duarte, Hao Su,reno Orzari, Thiago Tomei,Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, and Dimitrios Gunopoulos. Particle Cloud Generation with Message Passing Generative Adversarial Networks. 6 2021.
[44] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR (Poster), 2015.
[45] Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. CoRR, abs/1312.6114, 2014.
[46] Yann LeCun, Sumit Chopra, Raia Hadsell, Aurelio Ranzato, and Fu Jie Huang. A tutorial on energy-based learning. 2006.
[47] Julien Leissner-Martin, Taoli Cheng, and Jean-Francois Arguin. Qcd jet samples with particle flow constituents, July 2020.
[48] M. Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji. Graphebm: Molecular graph generation with energy-based models. ArXiv, abs/2102.00546, 2021.
[49] Weitang Liu, Xiaoyun Wang, John D. Owens, and Yixuan Li. Energy-based out-of-distribution detection. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc.
[50] Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan. Do deep generative models know what they don't know? ArXiv, abs/1810.09136, 2019.
[51] Subhajit Naskar, Pedram Rooshenas, Simeng Sun, Mohit Iyyer, and Andrew McCallum. Energy-based reranking: Improving neural machine translation using energy-based models. In ACL, 2021.
[52] Radford Neal. MCMC Using Hamiltonian Dynamics. In Handbook of Markov Chain Monte Carlo, pages 113-162. 2011.
[53] Radford M. Neal. Annealed importance sampling. Statistics and Computing, 11:125-139, 2001.
[54] Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, and Andrew Y. Ng. Learning deep energy models. In Proceedings of the 28th International Conference on International Conference on Machine Learning, ICML'11, page 1105-1112, Madison, WI, USA, 2011. Omnipress.
[55] Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu. On the anatomy of mcmc-based maximum likelihood learning of energy-based models. In AAAI, 2020.
[56] Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Learning non-convergent non-persistent short-run mcmc toward energy-based model. In NeurIPS, 2019.
[57] Frank Noé, Jonas Köhler, and Hao Wu. Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Science, 365, 2019.