2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CV2026
Vision-Language-Guided Pseudo-Labels for Unsupervised Domain Adaptation in Semantic Segmentation for Waste Sorting
Udo Schlegel, Shubhangi, Gabriel Dax +3
Obtaining labeled data for semantic segmentation in applied settings (e.g., autonomous driving, industrial waste sorting) is expensive and often infeasible at scale. We present a c…
cs.LG2024★ 2 cited
Position: Embracing Negative Results in Machine Learning
Florian Karl, Lukas Malte Kemeter, Gabriel Dax +1
Publications proposing novel machine learning methods are often primarily rated by exhibited predictive performance on selected problems. In this position paper we argue that predi…
cs.LG2024
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier +6
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research o…