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Given an unconditional diffusion model targeting a joint model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the denoising SDE after the fact.
On the theory of the Brownian motion
Uhlenbeck, G. E. and Ornstein, L. S. (1930) · 1930
Earlier work this paper cites.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Geman, S. and Geman, D. (1984) · 1984
Earlier work this paper cites.
Brownian Motion and Stochastic Calculus
Karatzas, I. and Shreve, S. E. (1991) · 1991
Earlier work this paper cites.
Diffusions, Markov Processes, and Martingales
Rogers, L. C. G. and Williams, D. (2000) · 2000
Earlier work this paper cites.
Using all Metropolis–Hastings proposals to estimate mean values
Tjelmeland, H. (2004) · 2004
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
Earlier work this paper cites.
The pseudo-marginal approach for efficient Monte Carlo computations
Andrieu, C. and Roberts, G. O. (2009) · 2009
Earlier work this paper cites.
Markov chains and stochastic stability
Meyn, S. P. and Tweedie, R. L. (2009) · 2009
Earlier work this paper cites.
Particle Markov chain Monte Carlo methods
Andrieu, C., Doucet, A., and Holenstein, R. (2010) · 2010
Earlier work this paper cites.
MCMC methods for functions: modifying old algorithms to make them faster
Cotter, S. L., Roberts, G. O., Stuart, A. M., and White, D. (2013) · 2013
Earlier work this paper cites.
A survey of the Schrödinger problem and some of its connections with optimal transport
Léonard, C. (2014) · 2014
Earlier work this paper cites.
Twisted particle filters
Whiteley, N. and Lee, A. (2014) · 2014
Earlier work this paper cites.
Chopin, N. and Singh, S. S. (2015) · 2015
Earlier work this paper cites.
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Doucet, A., Pitt, M. K., Deligiannidis, G., and Kohn, R. (2015) · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J. (2015) · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
Earlier work this paper cites.
Simulation of multivariate diffusion bridges
Bladt, M., Finch, S., and Sørensen, M. (2016) · 2016
Earlier work this paper cites.
SGDR: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F. (2017) · 2017
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Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers
Andrieu, C., Lee, A., and Vihola, M. (2018) · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q. (2018) · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O. (2018) · 2018
Cited alongside, same era.
An Introduction to Sequential Monte Carlo
Chopin, N. and Papaspiliopoulos, O. (2020) · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
Flax: A neural network library and ecosystem for JAX
Heek, J., Levskaya, A., Oliver, A., Ritter, M., Rondepierre, B., Steiner, A., and van Zee, M. (2023) · 2023
Later among the works it cites.
Conditional particle filters with bridge backward sampling
Karppinen, S., Singh, S. S., and Vihola, M. (2023) · 2023
Later among the works it cites.
Image restoration with mean-reverting stochastic differential equations
Luo, Z., Gustafsson, F. K., Zhao, Z., Sjölund, J., and Schön, T. B. (2023) · 2023
Later among the works it cites.
Computing Bayes: from then ‘til now
Martin, G. M., Frazier, D. T., and Robert, C. P. (2023) · 2023
Later among the works it cites.
Bayesian filtering and smoothing
Särkkä, S. and Svensson, L. (2023) · 2023
Later among the works it cites.
Diffusion Schrödinger bridge matching
Shi, Y., Bortoli, V. D., Campbell, A., and Doucet, A. (2023) · 2023
Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
Improved techniques for training score-based generative models
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Trippe, B. L., Yim, J., Tischer, D., Baker, D., Broderick, T., Barzilay, R., and Jaakkola, T. S. (2023) · 2023
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Exploring CLIP for assessing the look and feel of images
Wang, J., Chan, K. C., and Loy, C. C. (2023) · 2023
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Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., Trippe, B., Naesseth, C., Blei, D., and Cunningham, J. P. (2023) · 2023
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