ADAGE uses Channel-Group SHAP to quantify alignment between GeoAI model explanations and domain knowledge references in satellite-based flood mapping.
arXiv preprint arXiv:2106.12228
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Conditional attribution retrieves contextually similar normal states from VAE latent spaces and UMAP embeddings to explain time-series anomalies while preserving dependencies, improving root-cause accuracy on SWaT and MSDS benchmarks.
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Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping
ADAGE uses Channel-Group SHAP to quantify alignment between GeoAI model explanations and domain knowledge references in satellite-based flood mapping.
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Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Conditional attribution retrieves contextually similar normal states from VAE latent spaces and UMAP embeddings to explain time-series anomalies while preserving dependencies, improving root-cause accuracy on SWaT and MSDS benchmarks.