35 citations · 40 across the 11 of their papers we have counts for
15 papers
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…
Landscaping Linear Mode Connectivity
Sidak Pal Singh, Linara Adilova, Michael Kamp +3
The presence of linear paths in parameter space between two different network solutions in certain cases, i.e., linear mode connectivity (LMC), has garnered interest from both theo…
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective
Nils Philipp Walter, Linara Adilova, Jilles Vreeken +1
Flatness of the loss surface not only correlates positively with generalization, but is also related to adversarial robustness since perturbations of inputs relate non-linearly to…
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Fan Yang, Pierre Le Bodic, Michael Kamp +1
Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires to li…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
FAM: Relative Flatness Aware Minimization
Linara Adilova, Amr Abourayya, Jianning Li +5
Flatness of the loss curve around a model at hand has been shown to empirically correlate with its generalization ability. Optimizing for flatness has been proposed as early as 199…