3 citations · 4 across the 4 of their papers we have counts for
4 papers
Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
Xuxi Chen, Zhendong Wang, Daouda Sow +5
In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study st…
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Xuxi Chen, Yu Yang, Zhangyang Wang +1
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar genera…
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Shiwei Liu, Tianlong Chen, Zhenyu Zhang +4
Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large…
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation
Junjie Yang, Xuxi Chen, Tianlong Chen +2
Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by ``overfitting" specific task type, leadi…