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We provide the first polynomial-time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling.
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“Estimation of non-normalized statistical models by score matching”
Aapo Hyvärinen · 2005
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“Gradient flows in metric spaces and in the space of probability measures”, Lectures in Mathematics ETH Zürich
Luigi Ambrosio, Nicola Gigli and Giuseppe Savaré · 2008
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“A connection between score matching and denoising autoencoders”
Pascal Vincent · 2011
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“Degenerate Fokker–Planck equations: Bismut formula, gradient estimate and Harnack inequality”
Arnaud Guillin and Feng-Yu Wang · 2012
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“Optimal transport for applied mathematicians” Calculus of variations, PDEs, and modeling 87
Filippo Santambrogio · 2015
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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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Michalis. Titsias and Omiros Papaspiliopoulos · 2018
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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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“Structured denoising diffusion models in discrete state-spaces”
Jacob Austin et al · 2021
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“Diffusion Schrödinger bridge with applications to score-based generative modeling”
Valentin De, 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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“Variational diffusion models”
Diederik Kingma, Tim Salimans, Ben Poole and Jonathan Ho · 2021
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“Structured logconcave sampling with a restricted Gaussian oracle”
Yin Lee, Ruoqi Shen and Kevin Tian · 2021
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“Learning gradient fields for molecular conformation generation”
Chence Shi, Shitong Luo, Minkai Xu and Jian Tang · 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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“Score-based generative modeling through stochastic differential equations”
Yang Song et al · 2021
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“Score-based generative modeling in latent space”
Arash Vahdat, Karsten Kreis and Jan Kautz · 2021
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“Generative modeling with denoising auto-encoders and Langevin sampling”
Adam Block, Youssef Mroueh and Alexander Rakhlin · 2022
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“Localization schemes: a framework for proving mixing bounds for Markov chains”
Yuansi Chen and Ronen Eldan · 2022
“Convergence for score-based generative modeling with polynomial complexity”
Holden Lee, Jianfeng Lu and Yixin Tan · 2022
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“DPM-Solver: a fast ODE solver for diffusion probabilistic model sampling in around 10 steps”
Cheng Lu et al · 2022
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“Score-based generative models detect manifolds”
Jakiw Pidstrigach · 2022
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“Hierarchical text-conditional image generation with CLIP latents”
Aditya Ramesh et al · 2022
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“High-resolution image synthesis with latent diffusion models”
R. Rombach et al · 2022
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“Solving inverse problems in medical imaging with score-based generative models”
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“Improved analysis for a proximal algorithm for sampling”
Yongxin Chen, Sinho Chewi, Adil Salim and Andre Wibisono · 2022
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“Log-concave sampling” Book draft available at https://chewisinho.github.io/ , 2022
Sinho Chewi · 2022
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Hongrui Chen, Holden Lee and Jianfeng Lu · 2022
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“Come-closer-diffuse-faster: accelerating conditional diffusion models for inverse problems through stochastic contraction”
H. Chung, B. Sim and J. Ye · 2022
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“Convergence of denoising diffusion models under the manifold hypothesis”
Valentin De · 2022
Cited alongside, same era.
“Sampling from the Sherrington–Kirkpatrick Gibbs measure via algorithmic stochastic localization”
Ahmed El, Andrea Montanari and Mark Sellke · 2022
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Yang Song, Liyue Shen, Lei Xing and Stefano Ermon · 2022
Later among the works it cites.
“Convergence in KL divergence of the inexact Langevin algorithm with application to score-based generative models”
Andre Wibisono and Kaylee. Yang · 2022
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“Probability flow solution of the Fokker–Planck equation”
Nicholas. Boffi and Eric Vanden-Eijnden · 2023
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“Restoration-degradation beyond linear diffusions: a non-asymptotic analysis for DDIM-type samplers”
Sitan Chen, Giannis Daras and Alexandros. Dimakis · 2023
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“Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions”
Sitan Chen et al · 2023
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“Improved dimension dependence of a proximal algorithm for sampling”
Jiaojiao Fan, Bo Yuan and Yongxin Chen · 2023
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“A proximal algorithm for sampling”
Jiaming Liang and Yongxin Chen · 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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“Posterior sampling from the spiked models via diffusion processes”
Andrea Montanari and Yuchen Wu · 2023
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“Diffusion policies as an expressive policy class for offline reinforcement learning”
Zhendong Wang, Jonathan. Hunt and Mingyuan Zhou · 2023
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“Fast sampling of diffusion models with exponential integrator”
Qinsheng Zhang and Yongxin Chen · 2023
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