7 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2024
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery
Enneng Yang, Li Shen, Zhenyi Wang +5
Model merging-based multitask learning (MTL) offers a promising approach for performing MTL by merging multiple expert models without requiring access to raw training data. However…
cs.LG2024★ 7 cited
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
Enneng Yang, Li Shen, Guibing Guo +4
Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive compu…