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Denoising diffusion models have become ubiquitous for generative modeling.
Statistics of Random Processes. I. General theory. Translated by A. B. Aries , volume 5 of Appl. Math. (N. Y.)
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Time reversal of diffusions
Haussmann, U. G. and Pardoux, E · 1986
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Markov chain Monte Carlo maximum likelihood
Geyer, C · 1991
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Numerical Solution of Stochastic Differential Equations , volume 23 of Appl. Math. (N. Y.)
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Zero-variance principle for Monte Carlo algorithms
Assaraf, R. and Caffarel, M · 1999
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Neal, R. M · 2001
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Adam: A method for stochastic optimization
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JAX: composable transformations of Python+NumPy programs, 2018
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Lai, C.-H., Takida, Y., Murata, N., Uesaka, T., Mitsufuji, Y., and Ermon, S · 2022
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SIXO: Smoothing inference with twisted objectives
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Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., Trippe, B. L., Naesseth, C. A., Blei, D., and Cunningham, J. P · 2023
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Monte Carlo guided diffusion for Bayesian linear inverse problems
Cardoso, G., Idrissi, Y. J. E., Corff, S. L., and Moulines, E · 2024
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Huang, X., Dong, H., Hao, Y., Ma, Y., and Zhang, T · 2024
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