Michael S Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. arXiv preprint arXiv:2209.15571, 2022.
David Alvarez-Melis, Yair Schiff, and Youssef Mroueh. Optimizing functionals on the space of probabilities with input convex neural networks. arXiv preprint arXiv:2106.00774, 2021.
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré. Gradient flows: in metric spaces and in the space of probability measures. Springer Science & Business Media, 2005.
Brandon Amos, Lei Xu, and J Zico Kolter. Input convex neural networks. In International Conference on Machine Learning, pages 146-155. PMLR, 2017.
Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, and Stephan Günemann. Neural flows: Efficient alternative to neural odes. Advances in Neural Information Processing Systems, 34:21325-21337, 2021.
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert. Efficient and modular implicit differentiation. arXiv preprint arXiv:2105.15183, 2021.
Nicholas M Boffi and Eric Vanden-Eijnden. Probability flow solution of the fokker-planck equation. Machine Learning: Science and Technology, 4(3):035012, jul 2023. doi: 10.1088/2632-2153/ace2aa. URL https://dx.doi.org/10.1088/2632-2153/ace2aa.
Nicolas Bonneel, Michiel Van De Panne, Sylvain Paris, and Wolfgang Heidrich. Displacement interpolation using lagrangian mass transport. In Proceedings of the 2011 SIGGRAPH Asia conference, pages 1-12, 2011.
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs, 2018. URL http://github.com/google/jax.
Martin Burger, José A Carrillo, and Marie-Therese Wolfram. A mixed finite element method for nonlinear diffusion equations. *Kinetic & Related Models*, 3(1):59, 2010.
Jose A Carrillo, Alina Chertock, and Yanghong Huang. A finite-volume method for nonlinear nonlocal equations with a gradient flow structure. Communications in Computational Physics, 17 (1):233-258, 2015.
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. Advances in neural information processing systems, 31, 2018.
Dan Crisan and Terry Lyons. A particle approximation of the solution of the kushner-stratonovitch equation. Probability Theory and Related Fields, 115(4):549-578, 1999.
Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian, Charlotte Bunne, Geoff Davis, and Olivier Teboul. Optimal transport tools (ott): A jax toolbox for all things wasserstein. arXiv preprint arXiv:2201.12324, 2022.
Lauro Langosco di Langosco, Vincent Fortuin, and Heiko Strathmann. Neural variational gradient descent. arXiv preprint arXiv:2107.10731, 2021.
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016.
Stefan Elfwing, Eiji Uchibe, and Kenji Doya. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. *Neural Networks*, 107:3-11, 2018.
Jiaojiao Fan, Amirhossein Taghvaei, and Yongxin Chen. Variational Wasserstein gradient flow. arXiv preprint arXiv:2112.02424, 2021.