Database Paper Browser

Back to papers

METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

Summary: METER: base detector trained on historical central concepts and a hypernetwork that generates parameter shifts for rapid adaptation to new concepts, avoiding costly retraining. Lightweight evidential‑DL drift controller supplies interpretable, robust drift signals and yields improved OAD accuracy and latency. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13755
Venue
VLDB
Year
2024
Pagerank
5.9395463e-05
Overall Rank
4,762 | 66.88%
DOI
10.14778/3636218.3636233

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers