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Found 8 papers, 2 papers with code
MTD: Multi-Timestep Detector for Delayed Streaming Perception
Autonomous driving systems require real-time environmental perception to ensure user safety and experience.
A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization
Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process.
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.
pISTA-SENSE-ResNet for Parallel MRI Reconstruction
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time.
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.
