GraphCBMs extend concept bottleneck models by building latent concept graphs to model correlations between concepts, yielding better image classification accuracy, more informative structure for interpretability, and stronger intervention results.
Learning graphs from data: A signal representation perspective
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DEGfM is a dynamic elliptical graph factor model that performs Riemannian optimization on the Grassmann manifold with geodesic temporal regularization to infer time-varying precision matrices.
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Graph Concept Bottleneck Models
GraphCBMs extend concept bottleneck models by building latent concept graphs to model correlations between concepts, yielding better image classification accuracy, more informative structure for interpretability, and stronger intervention results.
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Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization
DEGfM is a dynamic elliptical graph factor model that performs Riemannian optimization on the Grassmann manifold with geodesic temporal regularization to infer time-varying precision matrices.