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Found 28 papers, 2 papers with code
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding
Soft-thresholding has been widely used in neural networks.
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.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
Resolution enhancement of NMR by decoupling with low-rank Hankel model
Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine.
Alternating Deep Low Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Recently, the low rankness of these exponentials has been applied to implicitly constrain the deep learning network through the unrolling of low rank Hankel factorization algorithm.
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
Physics-informed deep diffusion MRI reconstruction: break the bottleneck of training data in artificial intelligence
In this work, we propose a Physics-Informed Deep Diffusion magnetic resonance imaging (DWI) reconstruction method (PIDD).
Some Practice for Improving the Search Results of E-commerce
In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce.
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Deep learning has innovated the field of computational imaging.
Flow-level Coordination of Connected and Autonomous Vehicles in Multilane Freeway Ramp Merging Areas
The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions.
One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI).
Improving Freeway Merging Efficiency via Flow-Level Coordination of Connected and Autonomous Vehicles
Freeway on-ramps are typical bottlenecks in the freeway network due to the frequent disturbances caused by their associated merging, weaving, and lane-changing behaviors.
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
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.
TLab: Traffic Map Movie Forecasting Based on HR-NET
In our 2020 Competition solution, we further design multiple variants based on HR-NET and UNet.
Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension
In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge.
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.
Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
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.
A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI
The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging.
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.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
Projected Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
It has been shown that, redundant image representations, e. g. tight frames, can significantly improve the image quality.
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction
Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids.
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods.
