14 citations · 49 across the 16 of their papers we have counts for
4 papers · 1 filter
Improving Label Error Detection and Elimination with Uncertainty Quantification
Johannes Jakubik, Michael Vössing, Manil Maskey +2
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that…
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Johannes Jakubik, Daniel Weber, Patrick Hemmer +2
Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and c…
Learning to Defer with Limited Expert Predictions
Patrick Hemmer, Lukas Thede, Michael Vössing +2
Recent research suggests that combining AI models with a human expert can exceed the performance of either alone. The combination of their capabilities is often realized by learnin…
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Johannes Jakubik, Benedikt Blumenstiel, Michael Vössing +1
Business analytics and machine learning have become essential success factors for various industries - with the downside of cost-intensive gathering and labeling of data. Few-shot…