4 papers
A Free Lunch in LLM Compression: Revisiting Retraining after Pruning
Moritz Wagner, Christophe Roux, Max Zimmer +1
Post-training pruning can substantially reduce LLM inference costs, but it often degrades quality unless the remaining weights are adapted. Since global retraining is expensive at…
SparseSwaps: Tractable LLM Pruning Mask Refinement at Scale
Max Zimmer, Christophe Roux, Moritz Wagner +2
The resource requirements of neural networks can be significantly reduced through pruning - the removal of seemingly less important parameters. However, for LLMs, full retraining t…
S-CFE: Simple Counterfactual Explanations
Shpresim Sadiku, Moritz Wagner, Sai Ganesh Nagarajan +1
We study the problem of finding optimal sparse, manifold-aligned counterfactual explanations for classifiers. Canonically, this can be formulated as an optimization problem with mu…
GSE: Group-wise Sparse and Explainable Adversarial Attacks
Shpresim Sadiku, Moritz Wagner, Sebastian Pokutta
Sparse adversarial attacks fool deep neural networks (DNNs) through minimal pixel perturbations, often regularized by the norm. Recent efforts have replaced this norm with…