34 citations · 40 across the 5 of their papers we have counts for
5 papers
Improving Generalization in LLM Structured Pruning via Function-Aware Neuron Grouping
Tao Yu, Yongqi An, Kuan Zhu +3
Large Language Models (LLMs) demonstrate impressive performance across natural language tasks but incur substantial computational and storage costs due to their scale. Post-trainin…
Systematic Outliers in Large Language Models
Yongqi An, Xu Zhao, Tao Yu +2
Outliers have been widely observed in Large Language Models (LLMs), significantly impacting model performance and posing challenges for model compression. Understanding the functio…
Fluctuation-based Adaptive Structured Pruning for Large Language Models
Yongqi An, Xu Zhao, Tao Yu +2
Network Pruning is a promising way to address the huge computing resource demands of the deployment and inference of Large Language Models (LLMs). Retraining-free is important for…
Fast Segment Anything
Xu Zhao, Wenchao Ding, Yongqi An +5
The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like…
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
Yongqi An, Xu Zhao, Tao Yu +4
Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field…