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Scoring matching (SM), and its related counterpart, Stein discrepancy (SD) have achieved great success in model training and evaluations.
Information theory and the central limit theorem
Johnson, O · 2004
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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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
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A kernel test of goodness of fit
Chwialkowski, K., Strathmann, H., and Gretton, A · 2016
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Normalizing flows on riemannian manifolds
Gemici, M. C., Rezende, D., and Mohamed, S · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D · 2016
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A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M · 2016
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Measuring sample quality with Stein’s method
Gorham, J · 2017
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2018
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Hu, T., Chen, Z., Sun, H., Bai, J., Ye, M., and Cheng, G · 2018
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Measuring sample quality with diffusions
Gorham, J., Duncan, A. B., Vollmer, S. J., Mackey, L., et al · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Learning deep kernels for exponential family densities
Wenliang, L., Sutherland, D., Strathmann, H., and Gretton, A · 2019
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Learning the stein discrepancy for training and evaluating energy-based models without sampling
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., and Zemel, R · 2020
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Measure transport with kernel stein discrepancy
Fisher, M., Nolan, T., Graham, M., Prangle, D., and Oates, C · 2021
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Barp, A., Briol, F.-X., Duncan, A. B., Girolami, M., and Mackey, L · 2019
Cited alongside, same era.
Sliced score matching: A scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S
Cited in the paper.