Search Results for author:
Found 7 papers, 2 papers with code
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data.
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently.
Extra Proximal-Gradient Inspired Non-local Network
Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision.
Learning Backpropagation-Free Deep Architectures with Kernels
Moreover, unlike backpropagation, which turns models into black boxes, the optimal hidden representation enjoys an intuitive geometric interpretation, making the dynamics of learning in a deep kernel network simple to understand.
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples.
An Integrated Inverse Space Sparse Representation Framework for Tumor Classification
Extensive experiments on six public microarray gene expression datasets show the integrated ISSRC-based tumor classification framework is superior to classical and state-of-the-art methods.
On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation
With this method, we obtain a counterpart of any given NN that is powered by kernel machines instead of neurons.
