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.
Data-driven discovery of intrinsic dynamics.Nature Machine Intelligence, 4(12):1113–1120, 2022
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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.