[12] Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, and Jean-Roch Vlimant. Variational Autoencoders for New Physics Mining at the Large Hadron Collider. JHEP, 05:036, 2019.
[13] Tianqi Chen, Emily B. Fox, and Carlos Guestrin. Stochastic gradient hamiltonian monte carlo. In International Conference on Machine Learning, 2014.
[14] Taoli Cheng. Test sets for jet anomaly detection at the lhc, March 2021.
[15] Taoli Cheng, Jean-François Arguin, Julien Leissner-Martin, Jacinthe Pilette, and Tobias Golling. Variational autoencoders for anomalous jet tagging. Phys. Rev. D, 107(1):016002, 2023.
[16] Taoli Cheng and Aaron Courville. Invariant representation driven neural classifier for anti-QCD jet tagging. JHEP, 10:152, 2022.
[17] Jifeng Dai and Ying Nian Wu. Generative modeling of convolutional neural networks. CoRR, abs/1412.6296, 2014.
[18] J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi. Delphes 3: a modular framework for fast simulation of a generic collider experiment. Journal of High Energy Physics, 2014(2), Feb 2014.
[19] Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, and Marc'Aurelio Ranzato. Residual energy-based models for text generation. In International Conference on Learning Representations, 2020.
[20] Barry M. Dillon, Luigi Favaro, Tilman Plehn, Peter Sorrenson, and Michael Kramer. A normalized autoencoder for lhc triggers, 2022.
[21] Yilun Du, Shuang Li, and Igor Mordatch. Compositional visual generation and inference with energy based models.
[22] Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, and Igor Mordatch. Unsupervised learning of compositional energy concepts. In NeurIPS, 2021.
[23] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved contrastive divergence training of energy based models.
[24] Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives. Energy-based models for atomic-resolution protein conformations. In International Conference on Learning Representations, 2020.
[25] Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models.
[26] F. Englert and R. Brout. Broken symmetry and the mass of gauge vector mesons. Phys. Rev. Lett., 13:321-323, Aug 1964.
[27] Martin Erdmann, Jonas Glombitza, and Thorben Quast. Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network. Comput. Softw. Big Sci., 3(1):4, 2019.
[28] S. Chatrchyan et al. Observation of a new boson at a mass of 125GeV with the cms experiment at the lhc. Physics Letters B, 716(1):30-61, 2012.
[29] Thorben Finke, Michael Kramer, Alessandro Morandini, Alexander Mück, and Ivan Oleksiyuk. Autoencoders for unsupervised anomaly detection in high energy physics. JHEP, 06:161, 2021.
[30] J.A. Fodor and E. LePore. The Compositionality Papers. Clarendon Press, 2002.
[31] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.
[32] Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, and David Kristjanson Duvenaud. No mcmc for me: Amortized sampling for fast and stable training of energy-based models. ArXiv, abs/2010.04230, 2021.
[33] Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. Your classifier is secretly an energy based model and you should treat it like one.
[34] Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Xiaodong Song. Scaling out-of-distribution detection for real-world settings. In International Conference on Machine Learning, 2022.