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Diffusion models have revolutionized various application domains, including computer vision and audio generation.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
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The regularity of mappings with a convex potential
Luis A Caffarelli · 1992
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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An introduction to variational autoencoders
Diederik P Kingma, Max Welling, et al · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance
Jonathan Weed and Francis Bach · 2019
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Generative modeling with denoising auto-encoders and langevin sampling
Adam Block, Youssef Mroueh, and Alexander Rakhlin · 2020
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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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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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 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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Diffusion schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
Cited alongside, same era.
Linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2023
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The probability flow ode is provably fast
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, and Adil Salim · 2023
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Restoration-degradation beyond linear diffusions: A non-asymptotic analysis for ddim-type samplers
Sitan Chen, Giannis Daras, and Alex Dimakis · 2023
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Diffusion Policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Proteinsgm: Score-based generative modeling for de novo protein design
Jin Sub Lee, Jisun Kim, and Philip M Kim · 2022
Cited alongside, same era.
Latent diffusion for language generation
Justin Lovelace, Varsha Kishore, Chao Wan, Eliot Shekhtman, and Kilian Weinberger · 2022
Cited alongside, same era.
Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
Cited alongside, same era.
Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
Cited alongside, same era.
Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
Cited alongside, same era.
Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
Cited alongside, same era.
Minshuo Chen, Kaixuan Huang, Tuo Zhao, and Mengdi Wang · 2023
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Sampling from mean-field gibbs measures via diffusion processes
Ahmed El Alaoui, Andrea Montanari, and Mark Sellke · 2023
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Protein design with guided discrete diffusion
Nate Gruver, Samuel Stanton, Nathan C Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew Gordon Wilson · 2023
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IDQL: Implicit Q-learning as an actor-critic method with diffusion policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine · 2023
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Convergence guarantee for consistency models
Junlong Lyu, Zhitang Chen, and Shoubo Feng · 2023
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Diffusion models for image restoration and enhancement–a comprehensive survey
Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen · 2023
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Song Mei and Yuchen Wu · 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 models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Imitating human behaviour with diffusion models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, and Sam Devlin · 2023
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Goal-conditioned imitation learning using score-based diffusion policies
Moritz Reuss, Maximilian Li, Xiaogang Jia, and Rudolf Lioutikov · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang · 2023
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang · 2024
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