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We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale real-world generative models such as DALL$\cdot$E 2.
“An entropy approach to the time reversal of diffusion processes”
Hans Föllmer · 1985
Earlier work this paper cites.
“On sampling from a log-concave density using kinetic Langevin diffusions”
Arnak. Dalalyan and Lionel Riou-Durand · 1988
Earlier work this paper cites.
“Is there an analog of Nesterov acceleration for gradient-based MCMC?”
Yi-An Ma et al · 1992
Earlier work this paper cites.
“Comment on: “Hypercontractivity of Hamilton–Jacobi equations”, by S. G. Bobkov, I. Gentil and M. Ledoux”
Felix Otto and Cédric Villani · 2001
Earlier work this paper cites.
“Stochastic calculus and financial applications” 45
J. Steele · 2001
Earlier work this paper cites.
“Gradient flows: in metric spaces and in the space of probability measures”
Luigi Ambrosio, Nicola Gigli and Giuseppe Savaré · 2005
Earlier work this paper cites.
“Estimation of non-normalized statistical models by score matching”
Aapo Hyvärinen · 2005
Earlier work this paper cites.
“Hypocoercivity”
Cédric Villani · 2009
Earlier work this paper cites.
“A connection between score matching and denoising autoencoders”
Pascal Vincent · 2011
Earlier work this paper cites.
“Analysis and geometry of Markov diffusion operators” 348
Dominique Bakry, Ivan Gentil and Michel Ledoux · 2014
Earlier work this paper cites.
“Deep unsupervised learning using nonequilibrium thermodynamics”
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan and Surya Ganguli · 2015
Earlier work this paper cites.
“Brownian motion, martingales, and stochastic calculus” 274
Jean-François Le · 2016
Earlier work this paper cites.
“Statistical query lower bounds for robust estimation of high-dimensional Gaussians and Gaussian mixtures”
Ilias Diakonikolas, Daniel Kane and Alistair Stewart · 2017
Earlier work this paper cites.
“Underdamped Langevin MCMC: a non-asymptotic analysis”
Xiang Cheng, Niladri. Chatterji, Peter. Bartlett and Michael. Jordan · 2018
Earlier work this paper cites.
“Generative modeling by estimating gradients of the data distribution”
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
“The randomized midpoint method for log-concave sampling”
Ruoqi Shen and Yin Lee · 2019
Earlier work this paper cites.
“Rapid convergence of the unadjusted Langevin algorithm: isoperimetry suffices”
Santosh Vempala and Andre Wibisono · 2019
Earlier work this paper cites.
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