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Creating noise from data is easy; creating data from noise is generative modeling.
A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
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Giorgio Parisi · 1981
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Michael F Hutchinson · 1990
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Bernt Øksendal · 2003
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Aapo Hyvärinen · 2005
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Tweedie’s formula and selection bias
Bradley Efron · 2011
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Pascal Vincent · 2011
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Numerical continuation methods: an introduction , volume 13
Eugene L Allgower and Kurt Georg · 2012
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Numerical solution of stochastic differential equations , volume 23
Peter E Kloeden and Eckhard Platen · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep learning face attributes in the wild
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Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Applied stochastic differential equations , volume 10
Simo Särkkä and Arno Solin · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Sliced score matching: A scalable approach to density and score estimation
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Making convolutional networks shift-invariant again
Richard Zhang · 2019
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