Fetching the paper…
Reading the bibliography…
By learning the gradient of smoothed data distributions, diffusion models can iteratively generate samples from complex distributions.
Inverting modified matrices
Max A Woodbury · 1950
Earlier work this paper cites.
Attractive faces are only average
Judith H. Langlois and Lori A. Roggman · 1990
Earlier work this paper cites.
Eigenfaces for Recognition
Matthew Turk and Alex Pentland · 1991
Earlier work this paper cites.
The statistics of natural images
Daniel L Ruderman · 1994
Earlier work this paper cites.
Path integration and cognitive mapping in a continuous attractor neural network model
Alexei Samsonovich and Bruce L McNaughton · 1997
Earlier work this paper cites.
Introduction to electrodynamics, 2005
David J Griffiths · 2005
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Learning to be bayesian without supervision
Martin Raphan and Eero Simoncelli · 2006
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Classical electrodynamics
John David Jackson · 2012
Earlier work this paper cites.
Intrinsic dimension estimation: Advances and open problems
Francesco Camastra and Antonino Staiano · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Spectrum dependent learning curves in kernel regression and wide neural networks
Blake Bordelon, Abdulkadir Canatar, and Cengiz Pehlevan · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
Priorgrad: Improving conditional denoising diffusion models with data-dependent adaptive prior
Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, and Tie-Yan Liu · 2021
Cited alongside, same era.
Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
Later among the works it cites.
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
Later among the works it cites.
Generalization in diffusion models arises from geometry-adaptive harmonic representation
Zahra Kadkhodaie, Florentin Guth, Eero P Simoncelli, and Stéphane Mallat · 2023
Later among the works it cites.
Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, and Justin Solomon · 2023
Later among the works it cites.
Learning mixtures of gaussians using the ddpm objective
Kulin Shah, Sitan Chen, and Adam Klivans · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
A geometric analysis of deep generative image models and its applications
Binxu Wang and Carlos R Ponce · 2021
Cited alongside, same era.
Flexible diffusion modeling of long videos
William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Dietrich Weilbach, and Frank Wood · 2022
Cited alongside, same era.
Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Attractor and integrator networks in the brain
Mikail Khona and Ila R Fiete · 2022
Cited alongside, same era.
Later among the works it cites.
The Hidden Linear Structure in Score-Based Models and its Application
Binxu Wang and John J. Vastola · 2023
Later among the works it cites.
Diffusion models generate images like painters: an analytical theory of outline first, details later
Binxu Wang and John J Vastola · 2023
Later among the works it cites.
Score-based generative models learn manifold-like structures with constrained mixing
Li Kevin Wenliang and Ben Moran · 2023
Later among the works it cites.
Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2023
Later among the works it cites.
On the generalization of diffusion model
Mingyang Yi, Jiacheng Sun, and Zhenguo Li · 2023
Later among the works it cites.
The emergence of reproducibility and consistency in diffusion models
Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Liyue Shen, and Qing Qu · 2023
Later among the works it cites.
Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu · 2023
Later among the works it cites.
Learning mixtures of gaussians using diffusion models
Khashayar Gatmiry, Jonathan Kelner, and Holden Lee · 2024
Closest in time.
Diffusion models for gaussian distributions: Exact solutions and wasserstein errors
Emile Pierret and Bruno Galerne · 2024
Closest in time.
Unipc: A unified predictor-corrector framework for fast sampling of diffusion models
Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu · 2024
Closest in time.
Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks
Abdulkadir Canatar, Blake Bordelon, and Cengiz Pehlevan · 2041
Closest in time.