2 citations · 2 across the 4 of their papers we have counts for
4 papers
A Two-Sided Sketching Algorithm for Low-rank Tensor Train Approximation
Gaohang Yu, Yihao Pan, Ailun Jian +1
Tensor train (TT) decomposition is a powerful method to acquire low-rank tensors. However, the computational process is frequently obstructed by the large-scale matrix singular val…
GLT-PEFT: Gated Lie-Tucker Parameter-Efficient Fine-Tuning for Alzheimer's Disease Diagnosis with Hippocampal Segmentation Pretraining
Guanghua He, Hancan Zhu, Gaohang Yu +1
Parameter-efficient fine-tuning (PEFT) has emerged as a promising paradigm for adapting pretrained models under limited data conditions. However, most existing PEFT methods are des…
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation
Guanghua He, Wangang Cheng, Hancan Zhu +2
Transfer learning, by leveraging knowledge from pre-trained models, has significantly enhanced the performance of target tasks. However, as deep neural networks scale up, full fine…
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and Vectors
Guanghua He, Wangang Cheng, Hancan Zhu +1
The hippocampus is an important brain structure involved in various psychiatric disorders, and its automatic and accurate segmentation is vital for studying these diseases. Recentl…