Zhifeng Kong and Wei Ping. On fast sampling of diffusion probabilistic models. arXiv preprint arXiv:2106.00132, 2021.
Qiang Liu, Jason Lee, and Michael Jordan. A kernelized stein discrepancy for goodness-of-fit tests. In International conference on machine learning, pp. 276-284. PMLR, 2016.
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. arXiv preprint arXiv:2206.00927, 2022.
Eric Luhman and Troy Luhman. Knowledge distillation in iterative generative models for improved sampling speed. arXiv preprint arXiv:2101.02388, 2021.
L. M. Mescheder, S. Nowozin, and A. Geiger. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, 2017.
V. Moens, H. Ren, A. Maraval, R. Tutunov, J. Wang, and H. Ammar. Efficient semi-implicit variational inference. arXiv preprint arXiv:2101.06070, 2021.
Radford Neal. MCMC using hamiltonian dynamics. In S Brooks, A Gelman, G Jones, and XL Meng (eds.), Handbook of Markov Chain Monte Carlo, Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Taylor & Francis, 2011. ISBN 9781420079425. URL http://books.google.com/books?id=qfRsAIKZ4rIC.
Radford M Neal. Annealed importance sampling. Statistics and computing, 11:125-139, 2001.
Alex Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models, 2021.
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. Permutation invariant graph generation via score-based generative modeling. In International Conference on Artificial Intelligence and Statistics, 2020.
D. J. Nott, S. L. Tan, M. Villani, and R. Kohn. Regression density estimation with variational methods and stochastic approximation. Journal of Computational and Graphical Statistics, 21(3):797-820, 2012.
J. W. Paisley, D. M. Blei, and M. I. Jordan. Variational bayesian inference with stochastic search. In Proceedings of the 29th International Conference on Machine Learning ICML, 2012.
G. Papamakarios, E. Nalisnick, D. Rezende, S. Mohamed, and B. Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. ArXiv Preprint arXiv:1912.02762, 2019.
Fabian Pedregosa, Gáel Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine learning research, 12:2825–2830, 2011.
R. Ranganath, S. Gerrish, and D. M. Blei. Black box variational inference. In AISTATS, pp. 814-822, 2014.
Rajesh Ranganath, Dustin Tran, and David Blei. Hierarchical variational models. In International conference on machine learning, pp. 324-333. PMLR, 2016.
D. Rezende and S. Mohamed. Variational inference with normalizing flows. In Proceedings of The 32nd International Conference on Machine Learning, pp. 1530-1538, 2015.
D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning, 2014.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 2015.
Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. ArXiv, abs/2202.00512, 2022.