3 citations · 7 across the 5 of their papers we have counts for
5 papers
SkillSight: Calibrating Generic Content Bias for Skill Retrieval
Jinying Xiao, Bin Li, Xiaopeng Li +7
As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution. Existi…
EMP: Enhance Memory in Data Pruning
Jinying Xiao, Ping Li, Jie Nie +6
Recently, large language and vision models have shown strong performance, but due to high pre-training and fine-tuning costs, research has shifted towards faster training via datas…
TED: Accelerate Model Training by Internal Generalization
Jinying Xiao, Ping Li, Jie Nie
Large language models have demonstrated strong performance in recent years, but the high cost of training drives the need for efficient methods to compress dataset sizes. We propos…
LNPT: Label-free Network Pruning and Training
Jinying Xiao, Ping Li, Zhe Tang +1
Pruning before training enables the deployment of neural networks on smart devices. By retaining weights conducive to generalization, pruned networks can be accommodated on resourc…
SEVEN: Pruning Transformer Model by Reserving Sentinels
Jinying Xiao, Ping Li, Jie Nie +1
Large-scale Transformer models (TM) have demonstrated outstanding performance across various tasks. However, their considerable parameter size restricts their applicability, partic…