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A class of generative models that unifies flow-based and diffusion-based methods is introduced.
Estimation of the Mean of a Multivariate Normal Distribution
Charles M. Stein · 1981
Earlier work this paper cites.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
Earlier work this paper cites.
Classical potential theory and its probabilistic counterpart , volume 262 of Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences]
Joseph L. Doob · 1984
Earlier work this paper cites.
Diffusions hypercontractives
Dominique Bakry and Michel Émery · 1985
Earlier work this paper cites.
Exploratory projection pursuit
Jerome H. Friedman · 1987
Earlier work this paper cites.
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Yann Brenier · 1991
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Noise removal via bayesian wavelet coring
Eero P. Simoncelli and Edward H. Adelson · 1996
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
A family of nonparametric density estimation algorithms
Esteban G. Tabak and Cristina V. Turner · 2013
Earlier work this paper cites.
A survey of the schrödinger problem and some of its connections with optimal transport
Christian Léonard · 2014
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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Monge-Ampère flow for generative modeling
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Score-based generative modeling with critically-damped langevin diffusion
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An information-theoretic view of stochastic localization
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Convergence for score-based generative modeling with polynomial complexity
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Rectified flow: A marginal preserving approach to optimal transport
Qiang Liu · 2022
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