James Lucas, G. Tucker, Roger B. Grosse, and Mohammad Norouzi. Understanding posterior collapse in generative latent variable models. In DGS@ICLR, 2019.
Dimitra Maoutsa, Sebastian Reich, and Manfred Opper. Interacting particle solutions of fokkerplanck equations through gradient-log-density estimation. Entropy, 22(8):802, 2020.
Emile Mathieu and Maximilian Nickel. Riemannian continuous normalizing flows. Advances in Neural Information Processing Systems, 33:2503-2515, 2020.
Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Geneva, Justin M Solomon, and Evgeny Burnaev. Large-scale wasserstein gradient flows. Advances in Neural Information Processing Systems, 34:15243-15256, 2021.
Derek Onken, S Wu Fung, Xingjian Li, and Lars Ruthotto. Ot-flow: Fast and accurate continuous normalizing flows via optimal transport. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, 2021.
George Papamakarios, Theo Pavlakou, and Iain Murray. Masked autoregressive flow for density estimation. Advances in neural information processing systems, 30, 2017.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019.
Dmytro Perekrestenko, Stephan Müller, and Helmut Bölskei. Constructive universal high-dimensional distribution generation through deep relu networks. In International Conference on Machine Learning, pages 7610-7619. PMLR, 2020.
Dmytro Perekrestenko, Léandre Eberhard, and Helmut Bölskei. High-dimensional distribution generation through deep neural networks. *Partial Differential Equations and Applications*, 2(5): 1-44, 2021.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022.
Litu Rout, Alexander Korotin, and Evgeny Burnaev. Generative modeling with optimal transport maps. In International Conference on Learning Representations, 2022.
Lars Ruthotto, Stanley J Osher, Wuchen Li, Levon Nurbekyan, and Samy Wu Fung. A machine learning framework for solving high-dimensional mean field game and mean field control problems. Proceedings of the National Academy of Sciences, 117(17):9183-9193, 2020.
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016.
Antonin Schrab, Ilmun Kim, MÁlisande Albert, BÁ@atrice Laurent, Benjamin Guedj, and Arthur Gretton. Mmd aggregated two-sample test. Journal of Machine Learning Research, 24(194):1-81, 2023.
Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, and Hamed Hassani. Self-consistency of the fokker planck equation. In Conference on Learning Theory, pages 817-841. PMLR, 2022.
Thomas C Sideris. Ordinary differential equations and dynamical systems, volume 2. Springer, 2013.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015.