Summary of Thinner Latent Spaces: Detecting Dimension and Imposing Invariance Through Autoencoder Gradient Constraints, by George A. Kevrekidis et al.
Thinner Latent Spaces: Detecting dimension and imposing invariance through autoencoder gradient constraints
by George A. Kevrekidis, Mauro Maggioni, Soledad Villar, Yannis G. Kevrekidis
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Differential Geometry (math.DG); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Conformal Autoencoders impose orthogonality conditions between latent variables to achieve disentangled representations. This paper shows that orthogonality relations can be leveraged to infer intrinsic dimensionality and compute encoding/decoding maps for nonlinear manifold data. The method relies on differential geometry and a gradient-descent optimization algorithm, which is applied to standard datasets. The paper highlights the method’s applicability, advantages, and shortcomings, as well as its potential use in building coordinate invariance to local group actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal Autoencoders are a type of neural network that helps separate mixed-up data into meaningful pieces. This paper shows how this tool can also figure out how many dimensions are hidden in complex data sets and create a map to turn the data back into its original form. The method uses ideas from geometry and a special way of updating the model’s weights, which is applied to common datasets. The paper explains why this is useful and what it can be used for. |
Keywords
» Artificial intelligence » Gradient descent » Neural network » Optimization
