4 citations · 4 across the 6 of their papers we have counts for
6 papers
PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network
Bohui Shen, Wei Zhang, Xubiao Liu +8
Positron emission tomography (PET) serves as an essential tool for diagnosis of encephalopathy and brain science research. However, it suffers from the limited choice of tracers. N…
Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
Wei Zhang, Zengwei Xiao, Hui Tao +3
Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in hig…
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Zhongwei Wan, Yichun Yin, Wei Zhang +5
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs…
Collaboration of Pre-trained Models Makes Better Few-shot Learner
Renrui Zhang, Bohao Li, Wei Zhang +4
Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regim…
Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging
Hui Tao, Haifeng Wang, Shanshan Wang +3
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise a…
Compression of Generative Pre-trained Language Models via Quantization
Chaofan Tao, Lu Hou, Wei Zhang +5
The increasing size of generative Pre-trained Language Models (PLMs) has greatly increased the demand for model compression. Despite various methods to compress BERT or its variant…