Autoencoding Dynamics: Topological Limitations and Capabilities

Given a "data manifold" $M\subset \mathbb{R}^n$ and "latent space" $\mathbb{R}^\ell$, an autoencoder is a pair of continuous maps consisting of an "encoder" $E\colon \mathbb{R}^n\to \mathbb{R}^\ell$ and "decoder" $D\colon \mathbb{R}^\ell\to \mathbb{R}^n$ such that the "round trip" map $D\circ E$ is as close as possible to the identity map $\mbox{id}_M$ on $M$. We present various topological limitations and capabilites inherent to the search for an autoencoder, and describe capabilities for autoencoding dynamical systems having $M$ as an invariant manifold.

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Publication Details

Journal
Maryland Shared Open Access Repository (USMAI Consortium)
Published
2025-11-11
DOI
https://doi.org/10.13016/m2v1g1-abne
Primary Topic
Topological and Geometric Data Analysis
Type
article
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article

Autoencoding Dynamics: Topological Limitations and Capabilities

Eduardo D. Sontag, Matthew D. Kvalheim
Maryland Shared Open Access Repository (USMAI Consortium)
Topological and Geometric Data Analysis
article

Autoencoding Dynamics: Topological Limitations and Capabilities

Eduardo D. Sontag, Matthew D. Kvalheim
article en

Abstract

Given a "data manifold" $M\subset \mathbb{R}^n$ and "latent space" $\mathbb{R}^\ell$, an autoencoder is a pair of continuous maps consisting of an "encoder" $E\colon \mathbb{R}^n\to \mathbb{R}^\ell$ and "decoder" $D\colon \mathbb{R}^\ell\to \mathbb{R}^n$ such that the "round trip" map $D\circ E$ is as close as possible to the identity map $\mbox{id}_M$ on $M$. We present various topological limitations and capabilites inherent to the search for an autoencoder, and describe capabilities for autoencoding dynamical systems having $M$ as an invariant manifold.

Maryland Shared Open Access Repository (USMAI Consortium)
Openalex Percentile: Top 25%
Topological and Geometric Data Analysis
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