1 citations · 2 across the 4 of their papers we have counts for
4 papers
UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking
V. Bugra Yesilkaynak, Emine Dari, Alican Mertan +1
Existing multi-label ranking (MLR) frameworks only exploit information deduced from the bipartition of labels into positive and negative sets. Therefore, they do not benefit from r…
VISAGE: Video Synthesis using Action Graphs for Surgery
Yousef Yeganeh, Rachmadio Lazuardi, Amir Shamseddin +4
Surgical data science (SDS) is a field that analyzes patient data before, during, and after surgery to improve surgical outcomes and skills. However, surgical data is scarce, heter…
GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking
V. Bugra Yesilkaynak, Emine Dari, Alican Mertan +1
Existing multi-label frameworks only exploit the information deduced from the bipartition of the labels into a positive and negative set. Therefore, they do not benefit from the ra…
RLSEP: Learning Label Ranks for Multi-label Classification
Emine Dari, V. Bugra Yesilkaynak, Alican Mertan +1
Multi-label ranking maps instances to a ranked set of predicted labels from multiple possible classes. The ranking approach for multi-label learning problems received attention for…