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Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM).
A generalization of the gamma distribution
Stacy, E. W · 1962
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Reverse-time diffusion equation models
Anderson, B. D · 1982
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The fractal geometry of nature , volume 1
Mandelbrot, B. B. and Mandelbrot, B. B · 1982
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Sums of independent squared cauchy variables grow quadratically: Applications
Eicker, F · 1985
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Comments on “representations of knowledge in complex systems” by u. grenander and mi miller
Besag, J · 1994
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A new look at independence
Talagrand, M · 1996
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Annealed importance sampling
Neal, R. M · 2001
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The extended exponential power distribution and bayesian robustness
Choy, S. B. and Walker, S. G · 2003
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Stochastic differential equations
Øksendal, B · 2003
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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A generalized normal distribution
Nadarajah, S · 2005
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Bayesian inference in statistical analysis , volume 40
Box, G. E. and Tiao, G. C · 2011
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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 · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P · 2011
Earlier work this paper cites.
Efficient and accurate algorithms for the computation and inversion of the incomplete gamma function ratios
Gil, A., Segura, J., and Temme, N. M · 2012
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Radial bayesian neural networks: beyond discrete support in large-scale bayesian deep learning
Farquhar, S., Osborne, M. A., and Gal, Y · 2020
Later among the works it cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Normalizing flows: An introduction and review of current methods
Kobyzev, I., Prince, S., and Brubaker, M · 2020
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
Later among the works it cites.
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Cited alongside, same era.
Fractional langevin monte carlo: Exploring lévy driven stochastic differential equations for markov chain monte carlo
Şimşekli, U · 2017
Cited alongside, same era.
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
Cited alongside, same era.
Sharp convergence rates for langevin dynamics in the nonconvex setting
Cheng, X., Chatterji, N. S., Abbasi-Yadkori, Y., Bartlett, P. L., and Jordan, M. I · 2018
Cited alongside, same era.
Analytical properties of generalized gaussian distributions
Dytso, A., Bustin, R., Poor, H. V., and Shamai, S · 2018
Cited alongside, same era.
Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
Cited alongside, same era.
High-order langevin diffusion yields an accelerated mcmc algorithm
Mou, W., Ma, Y.-A., Wainwright, M. J., Bartlett, P. L., and Jordan, M. I · 2019
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
Closest in time.
A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A · 2021
Closest in time.
Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
Closest in time.
Score matching model for unbounded data score
Kim, D., Shin, S., Song, K., Kang, W., and Moon, I.-C · 2021
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Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
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Estimating high order gradients of the data distribution by denoising
Meng, C., Song, Y., Li, W., and Ermon, S · 2021
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Non gaussian denoising diffusion models
Nachmani, E., Roman, R. S., and Wolf, L · 2021
Closest in time.
Maximum likelihood training of score-based diffusion models
Song, Y., Durkan, C., Murray, I., and Ermon, S · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
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