30 citations · 61 across the 20 of their papers we have counts for
3 papers · 1 filter
: towards data-free Transferable Parameter Efficient Finetuning
Runqian Wang, Soumya Ghosh, David Cox +4
Low-rank adapters (LoRA) and their variants are popular parameter-efficient fine-tuning (PEFT) techniques that closely match full model fine-tune performance while requiring only a…
Adaptive Memory Replay for Continual Learning
James Seale Smith, Lazar Valkov, Shaunak Halbe +4
Foundation Models (FMs) have become the hallmark of modern AI, however, these models are trained on massive data, leading to financially expensive training. Updating FMs as new dat…
Learning to Grow Pretrained Models for Efficient Transformer Training
Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen +6
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic…