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Graph geometry-preserving autoencoders
When using an autoencoder to learn the low-dimensional manifold of high-dimensional
data, it is crucial to find the latent representations that preserve the geometry of the data …
data, it is crucial to find the latent representations that preserve the geometry of the data …
[PDF][PDF] Metric flow matching for smooth interpolations on the data manifold
Matching objectives underpin the success of modern generative models and rely on
constructing conditional paths that transform a source distribution into a target distribution …
constructing conditional paths that transform a source distribution into a target distribution …
Geometric Autoencoders--What You See is What You Decode
P Nazari, S Damrich, FA Hamprecht - ar** text-based robot trajectory generation models is made particularly difficult by the
small dataset size, high dimensionality of the trajectory space, and the inherent complexity of …
small dataset size, high dimensionality of the trajectory space, and the inherent complexity of …