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We give an improved theoretical analysis of score-based generative modeling.
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Pascal Vincent, A connection between score matching and denoising autoencoders , Neural Computation 23
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Yang Song and Stefano Ermon, Generative modeling by estimating gradients of the data distribution , Advances in Neural Information Processing Systems, vol. 32, 2019
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Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon, Sliced score matching: A scalable approach to density and score estimation , UAI, 2019
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Santosh S. Vempala and Andre Wibisono, Rapid convergence of the unadjusted langevin algorithm: Isoperimetry suffices , NeurIPS, 2019
2019
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Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, and Ben Poole, Score-based generative modeling through stochastic differential equations , International Conference on Learning Representations, 2020
2020
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Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg, Structured denoising diffusion models in discrete state-spaces , NeurIPS, 2021
2021
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2021
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Sinho Chewi, Murat A Erdogdu, Mufan Li, Ruoqi Shen, and Shunshi Zhang, Analysis of langevin monte carlo from poincare to log-sobolev , Proceedings of Thirty Fifth Conference on Learning Theory (Po-Ling Loh and Maxim Raginsky, eds.), Proceedings of Machine Learning Research, vol. 178, PMLR, 02–05 Jul 2022, pp. 1–2
2022
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Holden Lee, Jianfeng Lu, and Yixin Tan, Convergence for score-based generative modeling with polynomial complexity , 2022
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