2 citations · 5 across the 13 of their papers we have counts for
3 papers · 2 filters
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…
Don't Be Greedy, Just Relax! Pruning LLMs via Frank-Wolfe
Christophe Roux, Max Zimmer, Alexandre d'Aspremont +1
Pruning is a common technique to reduce the compute and storage requirements of Neural Networks. While conventional approaches typically retrain the model to recover pruning-induce…
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…