Effektive Priorisierung von informativen Merkmalen und Beispielen zur Verbesserung des tiefen Lernens in rauschigen Daten.
The author proposes a systematic framework to prioritize informative features and examples throughout the model development process, aiming to mitigate the negative impact of noisy data on deep learning applications.