5 citations · 23 across the 13 of their papers we have counts for
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cs.LG2024
Reconstruct the Pruned Model without Any Retraining
Pingjie Wang, Ziqing Fan, Shengchao Hu +3
Structured pruning is a promising hardware-friendly compression technique for large language models (LLMs), which is expected to be retraining-free to avoid the enormous retraining…
cs.LG2024★ 1 cited
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
Wanru Zhao, Yaxin Du, Nicholas Donald Lane +2
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further s…