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Denoising Score Matching estimates the score of a noised version of a target distribution by minimizing a regression loss and is widely used to train the popular class of Denoising Diffusion Models.
An empirical Bayes approach to statistics
Robbins, H. E. (1956) · 1956
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An empirical Bayes estimator of the mean of a normal population
Miyasawa, K. (1961) · 1961
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The convolution inequality for entropy powers
Blachman, N. (1965) · 1965
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
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Least squares estimation without priors or supervision
Raphan, M. and Simoncelli, E. P. (2011) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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A Wasserstein minimum velocity approach to learning unnormalized models
Wang, Z., Cheng, S., Yueru, L., Zhu, J., and Zhang, B. (2020) · 2020
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Diffusion Schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A. (2021) · 2021
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Non-denoising forward-time diffusions
Peluchetti, S. (2021) · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
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Riemannian score-based generative modelling
De Bortoli, V., Mathieu, E., Hutchinson, M., Thornton, J., Teh, Y. W., and Doucet, A. (2022) · 2022
Cited alongside, same era.
Riemannian diffusion models
Huang, C.-W., Aghajohari, M., Bose, J., Panangaden, P., and Courville, A. C. (2022) · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S. (2022) · 2022
Cited alongside, same era.
Denoising diffusion probabilistic models on so(3) for rotational alignment
Leach, A., Schmon, S. M., Degiacomi, M. T., and Willcocks, C. G. (2022) · 2022
Cited alongside, same era.
Let us build bridges: Understanding and extending diffusion generative models
Liu, X., Wu, L., Ye, M., and Liu, Q. (2022) · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M. (2023) · 2023
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Scaling Riemannian diffusion models
Lou, A., Xu, M., and Ermon, S. (2023) · 2023
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Generative diffusion models for lattice field theory
Wang, L., Aarts, G., and Zhou, K. (2023) · 2023
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De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · 2023
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Se(3) diffusion model with application to protein backbone generation
Yim, J., Trippe, B. L., De Bortoli, V., Mathieu, E., Doucet, A., Barzilay, R., and Jaakkola, T. (2023) · 2023
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Salimans, T. and Ho, J. (2022) · 2022
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Path integral sampler: a stochastic control approach for sampling
Zhang, Q. and Chen, Y. (2022) · 2022
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Stochastic interpolants: A unifying framework for flows and diffusions
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E. (2023) · 2023
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Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
Arts, M., Garcia Satorras, V., Huang, C.-W., Zugner, D., Federici, M., Clementi, C., Noe, F., Pinsler, R., and van den Berg, R. (2023) · 2023
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Time reversal of diffusion processes under a finite entropy condition
Cattiaux, P., Conforti, G., Gentil, I., and Léonard, C. (2023) · 2023
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Renormalizing diffusion models
Cotler, J. and Rezchikov, S. (2023) · 2023
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Inferring phase transitions and critical exponents from limited observations with thermodynamic maps
Herron, L., Mondal, K., Schneekloth, J. S., and Tiwary, P. (2023) · 2023
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Zhang, D., Chen, R. T. Q., Liu, C.-H., , A., and Bengio, Y. (2023) · 2023
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Towards predicting equilibrium distributions for molecular systems with deep learning
Zheng, S., He, J., Liu, C., Shi, Y., Lu, Z., Feng, W., Ju, F., Wang, J., Zhu, J., Min, Y., Zhang, H., Tang, S., Hao, H., Jin, P., Chen, C., Noé, F., Liu, H., and Liu, T.-Y. (2023) · 2023
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Iterated denoising energy matching for sampling from Boltzmann densities
Akhound-Sadegh, T., Rector-Brooks, J., Bose, A. J., Mittal, S., Lemos, P., Liu, C.-H., Sendera, M., Ravanbakhsh, S., Gidel, G., Bengio, Y., Malkin, N., and Tong, A. (2024) · 2024
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Reverse diffusion Monte Carlo
Huang, X., Dong, H., Hao, Y., Ma, Y., and Zhang, T. (2024) · 2024
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Particle denoising diffusion sampler
Phillips, A., Dang, H. D., Hutchinson, M., De Bortoli, V., Deligiannidis, G., and Doucet, A. (2024) · 2024
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Improved sampling via learned diffusions
Richter, L., Berner, J., and Liu, G.-H. (2024) · 2024
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