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Deep generative models provide a systematic way to learn nonlinear data distributions, through a set of latent variables and a nonlinear "generator" function that maps latent points into the input space.
Riemannian Geometry
M.P. do Carmo · 1992
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Probabilistic non-linear principal component analysis with Gaussian process latent variable models
Neil Lawrence · 2005
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Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
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Representation Learning: A Review and New Perspectives
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Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Back to the future: Radial basis function networks revisited
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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Disquisitiones generales circa superficies curvas
Carl Friedrich Gauss
Cited in the paper.