most citedConvergent autoencoder approximation of low bending and low distortion manifold embeddings

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2025

Geodesic Calculus on Implicitly Defined Latent Manifolds

Florine Hartwig, Josua Sassen, Juliane Braunsmann +2

Latent manifolds of autoencoders provide low-dimensional representations of data, which can be studied from a geometric perspective. We propose to describe these latent manifolds a…

math.NA2022★ 1 cited

Convergent autoencoder approximation of low bending and low distortion manifold embeddings

Juliane Braunsmann, Marko Rajković, Martin Rumpf +1

Autoencoders, which consist of an encoder and a decoder, are widely used in machine learning for dimension reduction of high-dimensional data. The encoder embeds the input data man…