We develop in this paper a multi-grade deep learning method for solving
...
We consider deep neural networks with a Lipschitz continuous activation
...
This paper introduces a successive affine learning (SAL) model for
const...
L^1 based optimization is widely used in image denoising, machine learni...
The current deep learning model is of a single-grade, that is, it learns...
More competent learning models are demanded for data processing due to
i...
We propose a sparse regularization model for inversion of incomplete Fou...
Deep neural networks, as a powerful system to represent high dimensional...
We investigated the imaging performance of a fast convergent ordered-sub...
Convergence of deep neural networks as the depth of the networks tends t...
We explore convergence of deep neural networks with the popular ReLU
act...
This article presents a new primal-dual weak Galerkin method for second ...
The goal of this paper is to develop a novel numerical method for effici...
Our aim was to enhance visual quality and quantitative accuracy of dynam...
A wide class of regularization problems in machine learning and statisti...