77 citations · 83 across the 14 of their papers we have counts for
4 papers · 1 filter
MGAA: Multi-Granular Adaptive Allocation fof Low-Rank Compression of LLMs
Guangyan Li, Yongqiang Tang, Wensheng Zhang
The enormous parameter scale of large language models (LLMs) has made model compression a research hotspot, which aims to alleviate computational resource demands during deployment…
LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models
Guangyan Li, Yongqiang Tang, Wensheng Zhang
Large language models (LLMs) show excellent performance in difficult tasks, but they often require massive memories and computational resources. How to reduce the parameter scale o…
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning
Lei Tian, Yongqiang Tang, Liangchen Hu +1
Domain adaptation manages to transfer the knowledge of well-labeled source data to unlabeled target data. Many recent efforts focus on improving the prediction accuracy of target p…
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization
Zelin Ren, Xuebing Yang, Yuchen Jiang +1
Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel…