52 citations · 114 across the 7 of their papers we have counts for
7 papers
A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization
Jian Cao, Chen Qian, Yihui Huang +5
Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF)…
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Yihui Huang, Zi Wang, Xinlin Zhang +5
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts. Removing these artifacts is a fundamental problem in signal processing and this task i…
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
Dicheng Chen, Wanqi Hu, Huiting Liu +13
Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of 1H-MRS is the low Signal-Noise Ratio (SNR). To improve the SNR, a typi…
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
Zi Wang, Di Guo, Zhangren Tu +9
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled expone…
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Yihui Huang, Jinkui Zhao, Zi Wang +3
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data ma…
pISTA-SENSE-ResNet for Parallel MRI Reconstruction
Tieyuan Lu, Xinlin Zhang, Yihui Huang +6
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampl…
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Xiaobo Qu, Yihui Huang, Hengfa Lu +5
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept…