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Score-based diffusion models have significantly advanced high-dimensional data generation across various domains, by learning a denoising oracle (or score) from datasets.
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Peter Bickel, Bo Li, and Thomas Bengtsson · 2008
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Bernt Oksendal · 2013
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Dominique Bakry, Ivan Gentil, and Michel Ledoux · 2014
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Inapproximability of the partition function for the antiferromagnetic ising and hard-core models
Andreas Galanis, Daniel Štefankovič, and Eric Vigoda · 2016
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Alex Barnett, Leslie Greengard, Andras Pataki, and Marina Spivak · 2017
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Sourav Chatterjee and Persi Diaconis · 2018
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A very simple proof of the LSI for high temperature spin systems
Roland Bauerschmidt and Thierry Bodineau · 2019
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Boaz Barak, Samuel Hopkins, Jonathan Kelner, Pravesh K Kothari, Ankur Moitra, and Aaron Potechin · 2019
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Dmitriy Kunisky, Alexander S Wein, and Afonso S Bandeira · 2019
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Sampling can be faster than optimization
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, and Michael I Jordan · 2019
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Taming correlations through entropy-efficient measure decompositions with applications to mean-field approximation
Ronen Eldan · 2020
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Denoising diffusion probabilistic models
Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2022
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Stochastic dynamics and the Polchinski equation: an introduction
Roland Bauerschmidt, Thierry Bodineau, and Benoit Dagallier · 2023
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Linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2023
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Operator norm bounds on the correlation matrix of the SK model at high temperature
Christian Brennecke, Changji Xu, and Horng-Tzer Yau · 2023
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, and Anru Zhang · 2023
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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 · 2020
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Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, and Thuy-Duong Vuong · 2021
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ILVR: Conditioning method for denoising diffusion probabilistic models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon · 2021
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Robust compressed sensing mri with deep generative priors
Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros G Dimakis, and Jon Tamir · 2021
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SNIPS: Solving noisy inverse problems stochastically
Bahjat Kawar, Gregory Vaksman, and Michael Elad · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye · 2023
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Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions
Hongrui Chen, Holden Lee, and Jianfeng Lu · 2023
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Score-based diffusion models as principled priors for inverse imaging
Berthy T Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L Bouman, and William T Freeman · 2023
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Optimality of Glauber dynamics for general-purpose Ising model sampling and free energy approximation
Dmitriy Kunisky · 2023
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Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2023
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A variational perspective on solving inverse problems with diffusion models
Morteza Mardani, Jiaming Song, Jan Kautz, and Arash Vahdat · 2023
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Loss-guided diffusion models for plug-and-play controllable generation
Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, and Arash Vahdat · 2023
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Practical and asymptotically exact conditional sampling in diffusion models
Luhuan Wu, Brian Trippe, Christian Naesseth, David Blei, and John P Cunningham · 2023
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Nearly d-linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2024
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Listening to the noise: Blind denoising with gibbs diffusion
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Divide-and-conquer posterior sampling for denoising diffusion priors
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