arXiv:2606. 18833v1 Announce Type: new Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.
arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.
arXiv:2606. 19255v1 Announce Type: new Abstract: Time series anomaly detection plays a crucial role in a wide range of real-world applications.
arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.
arXiv:2505. 03509v3 Announce Type: replace Abstract: Anomaly detection in large datasets is essential in astronomy and computer vision.
arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).