2 papers
cs.LG2023
Identifying Drivers of Predictive Aleatoric Uncertainty
Pascal Iversen, Simon Witzke, Katharina Baum +1
Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Ident…
cs.LG2023
SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data
Maximilian Kleissl, Lukas Drews, Benedict B. Heyder +5
Training sophisticated machine learning (ML) models requires large datasets that are difficult or expensive to collect for many applications. If prior knowledge about system dynami…