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Denoising diffusions are a powerful method to generate approximate samples from high-dimensional data distributions.
An Empirical Bayes Approach to Statistics
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Random walks in a convex body and an improved volume algorithm
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Isoperimetric problems for convex bodies and a localization lemma
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Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Brownian Motion, Martingales, and Stochastic Calculus
Jean-François Le Gall · 2016
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Eldan’s Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion
Yin Tat Lee and Santosh S Vempala · 2017
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Convergence of Langevin MCMC in KL-divergence
Xiang Cheng and Peter Bartlett · 2018
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High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
Alain Durmus and Éric Moulines · 2019
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Generative Modeling by Estimating Gradients of the Data Distribution
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Taming correlations through entropy-efficient measure decompositions with applications to mean-field approximation
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Structured Denoising Diffusion Models in Discrete State-Spaces
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Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Convergence in KL and Rényi Divergence of the Unadjusted Langevin Algorithm Using Estimated Score
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