45 citations · 59 across the 4 of their papers we have counts for
4 papers
Tensor Decomposition based Personalized Federated Learning
Qing Wang, Jing Jin, Xiaofeng Liu +3
Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data. However, due to FL'…
Using calibrator to improve robustness in Machine Reading Comprehension
Jing Jin, Houfeng Wang
Machine Reading Comprehension(MRC) has achieved a remarkable result since some powerful models, such as BERT, are proposed. However, these models are not robust enough and vulnerab…
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization
Jing Jin, Cai Liang, Tiancheng Wu +2
Recently, transformer-based language models such as BERT have shown tremendous performance improvement for a range of natural language processing tasks. However, these language mod…
BinaryBERT: Pushing the Limit of BERT Quantization
Haoli Bai, Wei Zhang, Lu Hou +6
The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In thi…