4 papers
Removing spurious minima for planar features by skip connections
Jakob Paul Zimmermann, Moritz Grillo, Andrei Balakin +1
Understanding loss landscapes is central to explaining neural-network training, yet their structure remains only partially understood even in simple models. We study the Gaussian p…
Playing the network backward: A Game Theoretic Attribution Framework
Jakob Paul Zimmermann, Jim Berend, Georg Loho +2
Attribution methods explain which input features drive a model's prediction, making them central to model debugging and mechanistic interpretability. Yet backward attribution metho…
Knowledge-Guided Failure Prediction: Detecting When Object Detectors Miss Safety-Critical Objects
Jakob Paul Zimmermann, Gerrit Holzbach, David Lerch
Object detectors deployed in safety-critical environments can fail silently, e.g. missing pedestrians, workers, or other safety-critical objects without emitting any warning. Tradi…
Hidden Monotonicity: Explaining Deep Neural Networks via their DC Decomposition
Jakob Paul Zimmermann, Georg Loho
It has been demonstrated in various contexts that monotonicity leads to better explainability in neural networks. However, not every function can be well approximated by a monotone…