Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

ArXi:2605.06955v1 Announce Type: cross Denoising score matching (DSM) provides a way to a neural network to recover the score function, defined as the gradient of the log density, from noise-corrupted samples. Once trained, the score magnitude at a test point reflects how consistent that point is with the learned distribution, making it a natural anomaly signal. The key practical challenge is selecting the perturbation scale: too little noise yields unstable score estimates in sparse regions, while too much erases local structure and weakens anomaly sensitivity.