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Found 3 papers, 1 papers with code
Identifying latent distances with Finslerian geometry
Generative models are often stochastic, causing the data space, the Riemannian metric, and the geodesics, to be stochastic as well.
Variational Autoencoders with Riemannian Brownian Motion Priors
Variational Autoencoders (VAEs) represent the given data in a low-dimensional latent space, which is generally assumed to be Euclidean.
Expected path length on random manifolds
Manifold learning seeks a low dimensional representation that faithfully captures the essence of data.
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