[38] Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz. Multimodal unsupervised image-to-image translation. In Proceedings of the European conference on computer vision (ECCV), pages 172-189, 2018.
[39] Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349, 2015.
[40] Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning structured output representation using deep conditional generative models. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28, 2015.
[41] Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. On density estimation with diffusion models. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
[42] Kushagra Pandey, Avideep Mukherjee, Piyush Rai, and Abhishek Kumar. DiffuseVAE: Efficient, controllable and high-fidelity generation from low-dimensional latents. Transactions on Machine Learning Research, 2022. ISSN 2835-8856.
[43] Arash Vahdat, Karsten Kreis, and Jan Kautz. Score-based generative modeling in latent space. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
[44] Casper Kaae Sønderby, Tapani Raiko, Lars Maalø e, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29, 2016.
[45] Michela Paganini, Luke de Oliveira, and Benjamin Nachman. CaloGAN: Simulating 3D High Energy Particle Showers in Multilayer Electromagnetic Calorimeters with Generative Adversarial Networks. Phys. Rev. D, 97(1):014021, 2018. doi: 10.1103/PhysRevD.97.014021.
[46] ATLAS Collaboration. Deep Generative Models for Fast Photon Shower Simulation in ATLAS. 10 2022.
[47] Simon Badger et al. Machine Learning and LHC Event Generation. SciPost Phys., 14:079, 2023. doi: 10.21468/SciPostPhys.14.4.079.
[48] Yadong Lu, Julian Collado, Daniel Whiteson, and Pierre Baldi. Sparse autoregressive models for scalable generation of sparse images in particle physics. Physical Review D, 103(3):036012, 2021.
[49] J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro. The Automated Computation of Tree-level and Next-to-leading Order Differential Cross Sections, and Their Matching to Parton Shower Simulations. JHEP, 07:079, 2014. doi: 10.1007/JHEP07(2014)079.
[50] Torbjörn Sjöstrand, Stefan Ask, Jesper R. Christiansen, Richard Corke, Nishita Desai, Philip Ilten, Stephen Mrenna, Stefan Prestel, Christine O. Rasmussen, and Peter Z. Skands. An introduction to PYTHIA 8.2. Comput. Phys. Commun., 191:159-177, 2015. doi: 10.1016/j.cpc.2015.01.024.
[51] J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaitre, A. Mertens, and M. Selvaggi. DELPHES 3, A Modular Framework for Fast Simulation of a Generic Collider Experiment. JHEP, 02:057, 2014. doi: 10.1007/JHEP02(2014)057.
[52] Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, and Ullrich Köthe. Invertible networks or partons to detector and back again. SciPost Phys., 9:074, 2020. doi: 10.21468/SciPostPhys.9.5.074.
[53] Lynton Ardizzone, Till Bungert, Felix Draxler, Ullrich Köthe, Jakob Kruse, Robert Schmier, and Peter Sorrenson. Framework for Easily Invertible Architectures (FrEIA), 2018-2022.