[4] George Papamakarios and Iain Murray. Fast  $\varepsilon$ -free inference of simulation models with Bayesian conditional density estimation. In Advances in neural information processing systems, 2016. arXiv:1605.06376.
[5] Jan-Matthis Lueckmann, Pedro J Gonçalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H Macke. Flexible statistical inference for mechanistic models of neural dynamics. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 1289-1299, 2017.
[6] David Greenberg, Marcel Nonnenmacher, and Jakob Macke. Automatic posterior transformation for likelihood-free inference. In International Conference on Machine Learning, pages 2404-2414. PMLR, 2019.
[7] Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, and Bernhard Schölkopf. Real-Time Gravitational Wave Science with Neural Posterior Estimation. Phys. Rev. Lett., 127(24):241103, 2021. arXiv:2106.12594, doi:10.1103/PhysRevLett.127.241103.
[8] Louis Sharrock, Jack Simons, Song Liu, and Mark Beaumont. Sequential neural score estimation: Likelihood-free inference with conditional score based diffusion models. arXiv preprint arXiv:2210.04872, 2022.
[9] Tomas Geffner, George Papamakarios, and Andriy Mnih. Score modeling for simulation-based inference. arXiv preprint arXiv:2209.14249, 2022.
[10] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[11] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
[12] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020.
[13] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 8780-8794. Curran Associates, Inc., 2021. URL: https://proceedings.neurips.cc/paper_files/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf.
[14] Jonathan Ho, Chitwan Sahara, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. J. Mach. Learn. Res., 23:47:1-47:33, 2022. URL: http://jmlr.org/papers/v23/21-0635.html.
[15] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
[16] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. CoRR, abs/2210.02747, 2022. arXiv:2210.02747, doi: 10.48550/arXiv.2210.02747.
[17] Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, and Gilles Louppe. Averting a crisis in simulation-based inference. arXiv preprint arXiv:2110.06581, 2021.
[18] Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. Neural ordinary differential equations. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 6572-6583, 2018. URL: https://proceedings.neurips.cc/paper/2018/bit/69386f6bb1dfed68692a24c8686939b9-Abstract.html.