58 citations · 107 across the 22 of their papers we have counts for
3 papers · 1 filter
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Mingyang Zhang, Hao Chen, Chunhua Shen +4
Large Language Models (LLMs), such as LLaMA and T5, have shown exceptional performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) has emerged to ch…
Across-Task Neural Architecture Search via Meta Learning
Jingtao Rong, Xinyi Yu, Mingyang Zhang +1
Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search(NAS). It is challenging to apply NAS in meta-learning scen…
RepNAS: Searching for Efficient Re-parameterizing Blocks
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
In the past years, significant improvements in the field of neural architecture search(NAS) have been made. However, it is still challenging to search for efficient networks due to…