AINeutralarXiv – CS AI · 9h ago6/10
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Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
Researchers introduce K-DSM, a kurtosis-based noise scaling method for denoising score matching that improves tabular anomaly detection without additional model complexity. The approach achieves state-of-the-art performance by adaptively setting noise levels per feature based on marginal distribution shape, reducing hyperparameter tuning burden in scenarios where anomalies are unknown.