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Closed-Form Spectral Regularization for Multi-Task Model Merging
Yongxian Wei, Runxi Cheng, Xingxuan Zhang +4
Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-developme…
Whoever Started the Interference Should End It: Guiding Data-Free Model Merging via Task Vectors
Runxi Cheng, Feng Xiong, Yongxian Wei +2
Model merging seeks to integrate task-specific expert models into a unified architecture while preserving multi-task generalization capabilities, yet parameter interference between…
Multi-Task Model Merging via Adaptive Weight Disentanglement
Feng Xiong, Runxi Cheng, Wang Chen +4
Model merging has recently gained attention as an economical and scalable approach to incorporate task-specific weights from various tasks into a unified multi-task model. For exam…
Learn To Learn More Precisely
Runxi Cheng, Yongxian Wei, Xianglong He +5
Meta-learning has been extensively applied in the domains of few-shot learning and fast adaptation, achieving remarkable performance. While Meta-learning methods like Model-Agnosti…