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20232026
most citedLiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging

1 citations · 1 across the 8 of their papers we have counts for

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cs.LG2024★ 1 cited

LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging

Ke Wang, Nikolaos Dimitriadis, Alessandro Favero +3

Fine-tuning pre-trained models has become the standard approach to endow them with specialized knowledge, but it poses fundamental challenges. In particular, \textit{(i)} fine-tuni…

cs.LG2024

Localizing Task Information for Improved Model Merging and Compression

Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez +2

Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task model, but their applicability is reduce…