3 citations · 5 across the 5 of their papers we have counts for
5 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…
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…
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection
George Papadimitriou, Hongwei Jin, Cong Wang +5
A computational workflow, also known as workflow, consists of tasks that must be executed in a specific order to attain a specific goal. Often, in fields such as biology, chemistry…
Mining Scientific Workflows for Anomalous Data Transfers
Huy Tu, George Papadimitriou, Mariam Kiran +4
Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the workflow…
Logical peering for interdomain networking on testbeds
Yuanjun Yao, Qiang Cao, Paul Ruth +3
Research testbed fabrics have potential to support long-lived, evolving, interdomain experiments, including opt-in application traffic across multiple campuses and edge sites. We p…