4 citations · 4 across the 2 of their papers we have counts for
5 papers
Quantifying Uncertainty in Error Consistency: Towards Reliable Behavioral Comparison of Classifiers
Thomas Klein, Sascha Meyen, Wieland Brendel +2
Benchmarking models is a key factor for the rapid progress in machine learning (ML) research. Thus, further progress depends on improving benchmarking metrics. A standard metric to…
It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Act
Kristof Meding
What constitutes a fair decision? This question is not only difficult for humans but becomes more challenging when Artificial Intelligence (AI) models are used. In light of discrim…
What constitutes a Deep Fake? The blurry line between legitimate processing and manipulation under the EU AI Act
Kristof Meding, Christoph Sorge
When does a digital image resemble reality? The relevance of this question increases as the generation of synthetic images -- so called deep fakes -- becomes increasingly popular.…
Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)
Kristof Meding, Luca M. Schulze Buschoff, Robert Geirhos +1
"The power of a generalization system follows directly from its biases" (Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems -- but to what degree have we un…
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Robert Geirhos, Kristof Meding, Felix A. Wichmann
A central problem in cognitive science and behavioural neuroscience as well as in machine learning and artificial intelligence research is to ascertain whether two or more decision…