2 citations · 4 across the 4 of their papers we have counts for
4 papers
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
Hongwei Jin, George Papadimitriou, Krishnan Raghavan +5
Anomaly detection in computational workflows is critical for ensuring system reliability and security. However, traditional rule-based methods struggle to detect novel anomalies. T…
Scaling on Frontier: Uncertainty Quantification Workflow Applications using ExaWorks to Enable Full System Utilization
Mikhail Titov, Robert Carson, Matthew Rolchigo +6
When running at scale, modern scientific workflows require middleware to handle allocated resources, distribute computing payloads and guarantee a resilient execution. While indivi…
Self-supervised Learning for Anomaly Detection in Computational Workflows
Hongwei Jin, Krishnan Raghavan, George Papadimitriou +4
Anomaly detection is the task of identifying abnormal behavior of a system. Anomaly detection in computational workflows is of special interest because of its wide implications in…
Data Integrity Error Localization in Networked Systems with Missing Data
Yufeng Xin, Shih-Wen Fu, Anirban Mandal +4
Most recent network failure diagnosis systems focused on data center networks where complex measurement systems can be deployed to derive routing information and ensure network cov…