70 citations · 138 across the 14 of their papers we have counts for
4 papers · 1 filter
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities
Julia Rosenzweig, Joachim Sicking, Sebastian Houben +2
An important pillar for safe machine learning (ML) is the systematic mitigation of weaknesses in neural networks to afford their deployment in critical applications. An ubiquitous…
Approaching Neural Network Uncertainty Realism
Joachim Sicking, Alexander Kister, Matthias Fahrland +5
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems such as autonomous vehicles. While stan…
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
Joachim Sicking, Maram Akila, Maximilian Pintz +3
Quantification of uncertainty is one of the most promising approaches to establish safe machine learning. Despite its importance, it is far from being generally solved, especially…