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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DATER is a new conceptual framework that analyzes six modern data architectures by historical context, defining features, and conformance to technical requirement dimensions.
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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.
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Data Architectures and their Technical Requirements (DATER)
DATER is a new conceptual framework that analyzes six modern data architectures by historical context, defining features, and conformance to technical requirement dimensions.