3 citations · 3 across the 3 of their papers we have counts for
3 papers
Bayesian Fine-tuning in Projected Subspaces
Viktar Dubovik, Patryk Marszałek, Jacek Tabor +1
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large models by decomposing weight updates into low-rank matrices, significantly reducing storage and computat…
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA
Patryk Marszałek, Klaudia Bałazy, Jacek Tabor +1
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large language models by decomposing weight updates into low-rank matrices, significantly reducing storage and…
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
Klaudia Bałazy, Mohammadreza Banaei, Karl Aberer +1
The growth of large language models underscores the need for parameter-efficient fine-tuning. Despite its popularity, LoRA encounters storage and computational challenges when depl…