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Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains.
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Sampling from a log-concave distribution with projected langevin monte carlo
Sébastien Bubeck, Ronen Eldan, and Joseph Lehec · 2018
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
W. Fedus, M. Rosca, B. Lakshminarayanan, A. M. Dai, S. Mohamed, and I. Goodfellow · 2018
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Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space
Nikolas Nüsken and Lorenz Richter · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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An optimal control perspective on diffusion-based generative modeling
Julius Berner, Lorenz Richter, and Karen Ullrich · 2022
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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 R Zhang · 2022
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Score-based diffusion meets annealed importance sampling
Arnaud Doucet, Will Grathwohl, Alexander G de G Matthews, and Heiko Strathmann · 2022
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Espen Bernton, Jeremy Heng, Arnaud Doucet, and Pierre E Jacob · 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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Rapid convergence of the unadjusted langevin algorithm: Isoperimetry suffices
Santosh Vempala and Andre Wibisono · 2019
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Double trouble in double descent: Bias and variance (s) in the lazy regime
Stéphane d’Ascoli, Maria Refinetti, Giulio Biroli, and Florent Krzakala · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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First hitting diffusion models
Mao Ye, Lemeng Wu, and Qiang Liu · 2022
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Mmd-fuse: Learning and combining kernels for two-sample testing without data splitting
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Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data
Minshuo Chen, Kaixuan Huang, Tuo Zhao, and Mengdi Wang · 2023
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Feature likelihood score: Evaluating generalization of generative models using samples, 2023
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Convergence of score-based generative modeling for general data distributions
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Diffusion models are minimax optimal distribution estimators
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Aligned diffusion schr \ \backslash " odinger bridges
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Denoising diffusion samplers
Francisco Vargas, Will Sussman Grathwohl, and Arnaud Doucet · 2023
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R-divergence for estimating model-oriented distribution discrepancy
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