10 citations · 21 across the 9 of their papers we have counts for
3 papers · 1 filter
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access t…
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen +2
Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as…
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical…