9 citations · 11 across the 7 of their papers we have counts for
3 papers · 1 filter
When Flatness Does (Not) Guarantee Adversarial Robustness
Nils Philipp Walter, Linara Adilova, Jilles Vreeken +1
Despite their empirical success, neural networks remain vulnerable to small, adversarial perturbations. A longstanding hypothesis suggests that flat minima, regions of low curvatur…
Fisher information flow in artificial neural networks
Maximilian Weimar, Lukas M. Rachbauer, Ilya Starshynov +4
The estimation of continuous parameters from measured data plays a central role in many fields of physics. A key tool in understanding and improving such estimation processes is th…
Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking
Ting Han, Linara Adilova, Henning Petzka +2
Neural collapse, i.e., the emergence of highly symmetric, class-wise clustered representations, is frequently observed in deep networks and is often assumed to reflect or enable ge…