11 citations · 23 across the 4 of their papers we have counts for
4 papers
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning
Yuchao Li, Fuli Luo, Chuanqi Tan +4
With the dramatically increased number of parameters in language models, sparsity methods have received ever-increasing research focus to compress and accelerate the models. While…
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient
Shaokun Zhang, Xiawu Zheng, Chenyi Yang +7
Despite superior performance on various natural language processing tasks, pre-trained models such as BERT are challenged by deploying on resource-constraint devices. Most existing…
Towards Compact CNNs via Collaborative Compression
Yuchao Li, Shaohui Lin, Jianzhuang Liu +7
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in…
1xN Pattern for Pruning Convolutional Neural Networks
Mingbao Lin, Yuxin Zhang, Yuchao Li +6
Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as w…