3 papers
eess.SP2026
Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
Emil Hardarson, Konstantin Popov, Sigridur Sigurdardottir +3
Automated sleep staging is commonly approached as a supervised machine learning problem, with deep learning methods dominating recent research. While machine learning models achiev…
cs.LG2026
Data-Local Autonomous LLM-Guided Neural Architecture Search for Multiclass Multimodal Time-Series Classification
Emil Hardarson, Luka Biedebach, Ómar Bessi Ómarsson +3
Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training of…
cs.HC2024
An Optimized Framework for Processing Large-scale Polysomnographic Data Incorporating Expert Human Oversight
Benedikt Holm, Gabriel Jouan, Emil Hardarson +6
Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning metho…