<ul><li>There has been a significant increase in the deployment of neural network models, presenting challenges in model adaptation and fine-tuning.</li><li>Low-Rank Adaptation (LoRA) has emerged as a promising parameter-efficient fine-tuning method.</li><li>This research proposes MetaLoRA, a novel parameter-efficient adaptation framework that integrates meta-learning principles.</li><li>MetaLoRA accurately captures task patterns by incorporating meta-learning mechanisms and dynamic parameter adjustment strategies.</li></ul>

MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning

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