arXiv Machine Learning August 17, 2026 By Junichiro Niimi

Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary

Read the original on arXiv Machine Learning →

arXiv:2608. 14186v1 Announce Type: new Abstract: Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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