Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
cs.LG
Submitted: 2026-08-30
Updated: 2026-08-30
Terminology
Sources
- Isometric Autoencoders
- Fully discrete analysis of the Galerkin POD neural network approximation with application to 3D acoustic wave scattering
- A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
- Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- A Geometric Perspective on Autoencoders
- Are We Using Autoencoders in a Wrong Way?
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Topological Autoencoders
- Linearly-Recurrent Autoencoder Networks for Learning Dynamics
- From Principal Subspaces to Principal Components with Linear Autoencoders
- The Intrinsic Dimension of Images and Its Impact on Learning
- A Discussion On the Validity of Manifold Learning
- A Kernel-Based Approach to Data-Driven Koopman Spectral Analysis
- Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems
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