RedZeD presents a new theoretical framework and algorithm for faster persistent homology computation on Vietoris-Rips filtrations.
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Persistent homology applied to fingerprint ridges produces multi-scale summaries that carry substantial identity information and enable effective verification outperforming pixel geometry baselines.
HOLE applies persistent homology to latent embeddings in neural networks and uses visualizations such as cluster flow diagrams to reveal patterns of class separation, feature disentanglement, and robustness.
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HOLE: Homological Observation of Latent Embeddings for Neural Network Interpretability
HOLE applies persistent homology to latent embeddings in neural networks and uses visualizations such as cluster flow diagrams to reveal patterns of class separation, feature disentanglement, and robustness.