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Neural networks can learn interpretable, polytope-based latent representations that preserve performance and enable direct VQ training.
Interpretable archetypes emerge from discrete data using a principled likelihood framework, unlocking insights in genomics and microbiome analysis previously inaccessible to standard AA.
