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Score-based generative modeling (SGM) is a highly successful approach for learning a probability distribution from data and generating further samples.
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Brian Anderson · 1982
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“On extensions of the Brunn-Minkowski and Prékopa-Leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation”
Herm Brascamp and Elliott Lieb · 2002
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“Entropies, convexity, and functional inequalities, On Φ \Phi -entropies and Φ \Phi -Sobolev inequalities”
Djalil Chafaï · 2004
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“A convex/log-concave correlation inequality for Gaussian measure and an application to abstract Wiener spaces”
Gilles Hargé · 2004
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“Analysis and geometry of Markov diffusion operators”
Dominique Bakry, Ivan Gentil and Michel Ledoux · 2013
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“Generative adversarial nets”
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville and Yoshua Bengio · 2014
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“Deep unsupervised learning using nonequilibrium thermodynamics”
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan and Surya Ganguli · 2015
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“Density estimation using real nvp”
Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2016
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“Energy-based generative adversarial network”
Junbo Zhao, Michael Mathieu and Yann LeCun · 2016
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“Wasserstein generative adversarial networks”
Martin Arjovsky, Soumith Chintala and Léon Bottou · 2017
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“Theoretical guarantees for approximate sampling from smooth and log-concave densities”
Arnak Dalalyan · 2017
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“Nonasymptotic convergence analysis for the unadjusted Langevin algorithm”
Alain Durmus and Eric Moulines · 2017
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“Convergence of Langevin MCMC in KL-divergence”
Xiang Cheng and Peter Bartlett · 2018
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“Underdamped Langevin MCMC: A non-asymptotic analysis”
Xiang Cheng, Niladri Chatterji, Peter Bartlett and Michael Jordan · 2018
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“Beyond log-concavity: provable guarantees for sampling multi-modal distributions using simulated tempering langevin monte carlo”
Rong Ge, Holden Lee and Andrej Risteski · 2018
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“Plug and play language models: A simple approach to controlled text generation”
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski and Rosanne Liu · 2019
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“User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient”
Arnak Dalalyan and Avetik Karagulyan · 2019
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“Your classifier is secretly an energy based model and you should treat it like one”
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi and Kevin Swersky · 2019
“Sliced score matching: A scalable approach to density and score estimation”
Yang Song, Sahaj Garg, Jiaxin Shi and Stefano Ermon · 2020
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“Score-Based Generative Modeling through Stochastic Differential Equations”
Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole · 2020
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“Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev”
Sinho Chewi, Murat Erdogdu, Mufan Li, Ruoqi Shen and Matthew Zhang · 2021
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“Diffusion Schrödinger bridge with applications to score-based generative modeling”
Valentin De Bortoli, James Thornton, Jeremy Heng and Arnaud Doucet · 2021
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“Diffusion models beat gans on image synthesis”
Prafulla Dhariwal and Alexander Nichol · 2021
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“An introduction to variational autoencoders”
Diederik Kingma and Max Welling · 2019
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Yang Song and Stefano Ermon · 2019
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“Theoretical guarantees for sampling and inference in generative models with latent diffusions”
Belinda Tzen and Maxim Raginsky · 2019
Cited alongside, same era.
“Rapid convergence of the unadjusted langevin algorithm: Isoperimetry suffices”
Santosh Vempala and Andre Wibisono · 2019
Cited alongside, same era.
“Generative modeling with denoising auto-encoders and Langevin sampling”
Adam Block, Youssef Mroueh and Alexander Rakhlin · 2020
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“Denoising diffusion probabilistic models”
Jonathan Ho, Ajay Jain and Pieter Abbeel · 2020
Cited alongside, same era.
“Nonasymptotic bounds for sampling algorithms without log-concavity”
Mateusz Majka, Aleksandar Mijatović and Łukasz Szpruch · 2020
Cited alongside, same era.
Tim Dockhorn, Arash Vahdat and Karsten Kreis · 2021
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“Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence”
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“Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows”
Holden Lee, Chirag Pabbaraju, Anish Sevekari and Andrej Risteski · 2021
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“SDEdit: Guided image synthesis and editing with stochastic differential equations”
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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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“Solving Inverse Problems in Medical Imaging with Score-Based Generative Models”
Yang Song, Liyue Shen, Lei Xing and Stefano Ermon · 2021
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“Subspace Diffusion Generative Models”
Bowen Jing, Gabriele Corso, Renato Berlinghieri and Tommi Jaakkola · 2022
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