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.
Schonsheck, Scott Mahan, Timo Klock, Alexander Cloninger, and Rongjie Lai
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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.