Score matching



Very famous now thanks to neural diffusions.

Basic

References

Hyvärinen, Aapo. 2005. Estimation of Non-Normalized Statistical Models by Score Matching.” The Journal of Machine Learning Research 6 (December): 695–709.
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.
Vincent, Pascal. 2011. A connection between score matching and denoising autoencoders.” Neural Computation 23 (7): 1661–74.

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