Fetching the paper…
Reading the bibliography…
In a great number of tasks in science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain sensing or imaging modality.
The Brownian movement and stochastic equations
Doob, J. L. (1942) · 1942
Earlier work this paper cites.
Cubic convolution interpolation for digital image processing
Keys, R. (1981) · 1981
Earlier work this paper cites.
Reverse-time diffusion equation models
Anderson, B. D. (1982) · 1982
Earlier work this paper cites.
Markov Chains for Exploring Posterior Distributions
Tierney, L. (1994) · 1994
Earlier work this paper cites.
Lectures on finite Markov chains
Saloff-Coste, L. (1997) · 1997
Earlier work this paper cites.
Optimal scaling of discrete approximations to Langevin diffusions
Roberts, G. O. and Rosenthal, J. S. (1998) · 1998
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Ambrosio, L., Gigli, N., and Savaré, G. (2005) · 2005
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
Earlier work this paper cites.
Compressed sensing
Donoho, D. L. (2006) · 2006
Earlier work this paper cites.
Phase retrieval via Wirtinger flow: Theory and algorithms
Candes, E. J., Li, X., and Soltanolkotabi, M. (2015) · 2007
Earlier work this paper cites.
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., and Pauly, J. M. (2007) · 2007
Earlier work this paper cites.
Learning fast approximations of sparse coding
Gregor, K. and LeCun, Y. (2010) · 2010
Earlier work this paper cites.
Sparsity and compressed sensing in radar imaging
Potter, L. C., Ertin, E., Parker, J. T., and Cetin, M. (2010) · 2010
Earlier work this paper cites.
Tweedie’s formula and selection bias
Efron, B. (2011) · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
Earlier work this paper cites.
Exact matrix completion via convex optimization
Candes, E. and Recht, B. (2012) · 2012
Earlier work this paper cites.
An introduction to stochastic differential equations
Evans, L. C. (2012) · 2012
Earlier work this paper cites.
Banach Lattices and Positive Operators
Schaefer, H. (2012) · 2012
Earlier work this paper cites.
Plug-and-play priors for model based reconstruction
Venkatakrishnan, S. V., Bouman, C. A., and Wohlberg, B. (2013) · 2013
Earlier work this paper cites.
Proximal algorithms
Parikh, N. and Boyd, S. (2014) · 2014
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015) · 2015
Earlier work this paper cites.
Phase retrieval with application to optical imaging: a contemporary overview
Shechtman, Y., Eldar, Y. C., Cohen, O., Chapman, H. N., Miao, J., and Segev, M. (2015) · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Earlier work this paper cites.
Compressed sensing using generative models
Bora, A., Jalal, A., Price, E., and Dimakis, A. G. (2017) · 2017
Earlier work this paper cites.
The proximal point method revisited
Drusvyatskiy, D. (2017) · 2017
Cited alongside, same era.
Super-resolution image reconstruction for high-density three-dimensional single-molecule microscopy
Huang, J., Sun, M., Ma, J., and Chi, Y. (2017) · 2017
Cited alongside, same era.
The little engine that could: Regularization by denoising (RED)
Romano, Y., Elad, M., and Milanfar, P. (2017) · 2017
Cited alongside, same era.
Regularization by denoising: Clarifications and new interpretations
Reehorst, E. T. and Schniter, P. (2018) · 2018
Cited alongside, same era.
Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2018) · 2018
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T. (2019) · 2019
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al. (2022) · 2022
Later among the works it cites.
Pseudoinverse-guided diffusion models for inverse problems
Song, J., Vahdat, A., Mardani, M., and Kautz, J. (2022) · 2022
Later among the works it cites.
Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Trippe, B. L., Yim, J., Tischer, D., Baker, D., Broderick, T., Barzilay, R., and Jaakkola, T. (2022) · 2022
Later among the works it cites.
Efficient MCMC sampling with dimension-free convergence rate using ADMM-type splitting
Vono, M., Paulin, D., and Doucet, A. (2022) · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Nonconvex sampling with the metropolis-adjusted langevin algorithm
Mangoubi, O. and Vishnoi, N. K. (2019) · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
Cited alongside, same era.
Split-and-augmented Gibbs sampler-application to large-scale inference problems
Vono, M., Dobigeon, N., and Chainais, P. (2019) · 2019
Cited alongside, same era.
Plug-and-play unplugged: Optimization-free reconstruction using consensus equilibrium
Buzzard, G. T., Chan, S. H., Sreehari, S., and Bouman, C. A. (2018) · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2020) · 2020
Cited alongside, same era.
Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y. (2022) · 2022
Later among the works it cites.
Faster high-accuracy log-concave sampling via algorithmic warm starts
Altschuler, J. M. and Chewi, S. (2023) · 2023
Later among the works it cites.
Generative plug and play: Posterior sampling for inverse problems
Bouman, C. A. and Buzzard, G. T. (2023) · 2023
Later among the works it cites.
Monte carlo guided diffusion for Bayesian linear inverse problems
Cardoso, G., Idrissi, Y. J. E., Corff, S. L., and Moulines, E. (2023) · 2023
Later among the works it cites.
Plug-and-play split Gibbs sampler: embedding deep generative priors in Bayesian inference
Coeurdoux, F., Dobigeon, N., and Chainais, P. (2023) · 2023
Later among the works it cites.
Score-based diffusion models as principled priors for inverse imaging
Feng, B. T., Smith, J., Rubinstein, M., Chang, H., Bouman, K. L., and Freeman, W. T. (2023) · 2023
Later among the works it cites.
Statistical efficiency of score matching: The view from isoperimetry
Koehler, F., Heckett, A., and Risteski, A. (2023) · 2023
Later among the works it cites.
Towards faster non-asymptotic convergence for diffusion-based generative models
Li, G., Wei, Y., Chen, Y., and Chi, Y. (2023) · 2023
Later among the works it cites.
A variational perspective on solving inverse problems with diffusion models
Mardani, M., Song, J., Kautz, J., and Vahdat, A. (2023) · 2023
Later among the works it cites.
Provable probabilistic imaging using score-based generative priors
Sun, Y., Wu, Z., Chen, Y., Feng, B. T., and Bouman, K. L. (2023) · 2023
Later among the works it cites.
Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., Trippe, B., Naesseth, C., Blei, D., and Cunningham, J. P. (2023) · 2023
Later among the works it cites.
Nearly d d -linear convergence bounds for diffusion models via stochastic localization
Benton, J., De Bortoli, V., Doucet, A., and Deligiannidis, G. (2024) · 2024
Closest in time.
Diffusion posterior sampling for linear inverse problem solving: A filtering perspective
Dou, Z. and Song, Y. (2024) · 2024
Closest in time.
What’s in a prior? Learned proximal networks for inverse problems
Fang, Z., Buchanan, S., and Sulam, J. (2024) · 2024
Closest in time.
Diffusion posterior sampling is computationally intractable
Gupta, S., Jalal, A., Parulekar, A., Price, E., and Xun, Z. (2024) · 2024
Closest in time.
Provable benefits of score matching
Pabbaraju, C., Rohatgi, D., Sevekari, A. P., Lee, H., Moitra, A., and Risteski, A. (2024) · 2024
Closest in time.
Information theory: From coding to learning
Polyanskiy, Y. and Wu, Y. (2024) · 2024
Closest in time.
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Wibisono, A. (2018) · 2093
Closest in time.