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Diffusion models are powerful generative models that simulate the reverse of diffusion processes using score functions to synthesize data from noise.
On the theory of Brownian motion
Paul Langevin · 1908
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Equations of state calculations by fast computing machines
Nicholas Metropolis, Arianna W. Rosenbluth, Marshall N. Rosenbluth, Augusta H. Teller, and Edward Teller · 1953
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Monte Carlo sampling methods using Markov chains and their application
Wilfred K. Hastings · 1970
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Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Stuart Geman and Donald Geman · 1984
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Annealing Markov chain Monte Carlo with applications to ancestral inferences
Charles J. Geyer and Elizabeth A. Thompson · 1995
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Annealed importance sampling
Radford M. Neal · 2001
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Optimal scaling for various Metropolis-Hastings algorithms
Gareth O. Roberts and Jeffrey S. Rosenthal · 2001
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Introduction to Numerical Analysis , volume 12
Josef Stoer and Roland Bulisch · 2002
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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MCMC using Hamiltonian dynamics
Radford M. Neal · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Theoretical guarantees for approximate sampling from smooth and log-concave densities
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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Gotta go fast when generating data with score-based models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, and Ioannis Mitliagkas · 2021
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How to train your energy-based models
Yang Song and Diederik P. Kingma · 2021
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Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Generalizing Hamiltonian Monte Carlo with neural networks
Daniel Levy, Matthew D. Hoffman, and Jascha Sohl-Dickstein · 2018
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Log-concave sampling: Metropolis-Hastings algorithms are fast
Raaz Dwivedi, Yuansi Chen, Martin J.Wainwright, and Bin Yu · 2019
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole
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Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Soft truncation: A universal truncation technique of score-based diffusion for high precision score estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, and Il-Chul Moon · 2022
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Fast sampling of diffusion models with exponential integrator
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