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cs.CL2024★ 2 cited
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Didi Zhu, Zhongyi Sun, Zexi Li +5
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a sig…
cs.LG2024
FedGuCci: Making Local Models More Connected in Landscape for Federated Learning
Zexi Li, Jie Lin, Zhiqi Li +5
Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's glo…
cs.LG2024
Improving Model Fusion by Training-time Neuron Alignment with Fixed Neuron Anchors
Zexi Li, Zhiqi Li, Jie Lin +5
Model fusion aims to integrate several deep neural network (DNN) models' knowledge into one by fusing parameters, and it has promising applications, such as improving the generaliz…