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High-dimensional data often lies near a low-dimensional structure. New VAEs can extract this, but handling missing data is a challenge. This work may improve data imputation by assuming data lies on a manifold.
“arXiv:2607.03641v2 Announce Type: replace-cross Abstract: The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold. Recent advances in mixture variational autoencoders (VAEs) provid…”
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ACTIVE
CATEGORY
Research
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ENTITY
Mixture Variational Autoencoders (VAEs), Manifold Hypothesis
DECISION
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LAST OBSERVED
Aug 8, 2026