Tangent-bundle and inverse-consistency penalties derived from observed covariance improve autoencoder learning of nonlinear charts and latent SDEs, reducing radial mean first-passage time errors by 50-70% on embedded surfaces.
Local conformal autoencoder for standardized data coor- dinates.Proceedings of the National Academy of Sciences, 117(49):30918–30927, 2020
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Geometric regularization of autoencoders via observed stochastic dynamics
Tangent-bundle and inverse-consistency penalties derived from observed covariance improve autoencoder learning of nonlinear charts and latent SDEs, reducing radial mean first-passage time errors by 50-70% on embedded surfaces.