K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
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GNN embeddings turn CEBAF injector snapshots into a coordinate system revealing ten persistent operating regimes that support monitoring, outlier detection, and case-based reasoning over a year of data.
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Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
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Machine-State Embeddings as an Operational Coordinate System for Accelerator Operation
GNN embeddings turn CEBAF injector snapshots into a coordinate system revealing ten persistent operating regimes that support monitoring, outlier detection, and case-based reasoning over a year of data.