Score matching

Very famous now thanks to neural diffusions.



Dockhorn, Tim, Arash Vahdat, and Karsten Kreis. 2022. GENIE: Higher-Order Denoising Diffusion Solvers.” In.
Holzschuh, Benjamin, Simona Vegetti, and Nils Thuerey. 2022. “Score Matching via Differentiable Physics,” 7.
Hyvärinen, Aapo. 2005. Estimation of Non-Normalized Statistical Models by Score Matching.” The Journal of Machine Learning Research 6 (December): 695–709.
Lim, Jae Hyun, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, et al. 2023. Score-Based Diffusion Models in Function Space.” arXiv.
McAllester, David. 2023. On the Mathematics of Diffusion Models.” arXiv.
Sharrock, Louis, Jack Simons, Song Liu, and Mark Beaumont. 2022. Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models.” arXiv.
Sohl-Dickstein, Jascha, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” arXiv:1503.03585 [Cond-Mat, q-Bio, Stat], November.
Song, Jiaming, Chenlin Meng, and Stefano Ermon. 2021. Denoising Diffusion Implicit Models.” arXiv:2010.02502 [Cs], November.
Song, Yang, and Stefano Ermon. 2020a. Generative Modeling by Estimating Gradients of the Data Distribution.” In Advances In Neural Information Processing Systems. arXiv.
———. 2020b. Improved Techniques for Training Score-Based Generative Models.” In Advances In Neural Information Processing Systems. arXiv.
Song, Yang, Sahaj Garg, Jiaxin Shi, and Stefano Ermon. 2019. Sliced Score Matching: A Scalable Approach to Density and Score Estimation.” arXiv.
Song, Yang, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2022. Score-Based Generative Modeling Through Stochastic Differential Equations.” In.
Swersky, Kevin, Marc’Aurelio Ranzato, David Buchman, Nando D. Freitas, and Benjamin M. Marlin. 2011. “On Autoencoders and Score Matching for Energy Based Models.” In Proceedings of the 28th International Conference on Machine Learning (ICML-11), 1201–8.
Tran, Ba-Hien, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V Bonilla, and Maurizio Filippone. 2021. Model Selection for Bayesian Autoencoders.” In Advances in Neural Information Processing Systems, 34:19730–42. Curran Associates, Inc.
Vincent, Pascal. 2011. A connection between score matching and denoising autoencoders.” Neural Computation 23 (7): 1661–74.
Zhuang, Peiye, Samira Abnar, Jiatao Gu, Alex Schwing, Joshua M. Susskind, and Miguel Ángel Bautista. 2022. Diffusion Probabilistic Fields.” In.

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