국제학회 논문
GAPS: Gradient-Aware Adaptation-Gap Scoring for Time-Series Anomaly Detection with Foundation Models
Research summary
어떤 질문을 다루나요?
파운데이션 모델을 활용한 시계열 이상 탐지를 위한 Gradient-Aware Adaptation-Gap Scoring 연구입니다.
인용 정보
(2026). GAPS: Gradient-Aware Adaptation-Gap Scoring for Time-Series Anomaly Detection with Foundation Models. NeurIPS, The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2026.
BibTeX
@inproceedings{LST_gaps_2026,
title = {GAPS: Gradient-Aware Adaptation-Gap Scoring for Time-Series Anomaly Detection with Foundation Models},
author = {Kim, J. and Song, B. and Ko, Y. M.},
booktitle = {The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS)},
year = {2026}
}