2 papers
cs.LG2025
Robust Classification under Noisy Labels: A Geometry-Aware Reliability Framework for Foundation Models
Ecem Bozkurt, Antonio Ortega
Foundation models (FMs) pretrained on large datasets have become fundamental for various downstream machine learning tasks, in particular in scenarios where obtaining perfectly lab…
cs.LG2025
AutoML for Multi-Class Anomaly Compensation of Sensor Drift
Melanie Schaller, Mathis Kruse, Antonio Ortega +2
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as…