This paper is devoted to studying the optimal expressive power of ReLU d...
In this paper, we study linear regression applied to data structured on ...
By investigating iterative methods for a constrained linear model, we pr...
This study used a multigrid-based convolutional neural network architect...
The low dimensional manifold hypothesis posits that the data found in ma...
We propose a constrained linear data-feature-mapping model as an
interpr...
This paper is devoted to establishing L^2 approximation properties for d...
Weight initialization plays an important role in training neural network...
Compressed Sensing using 𝓁1 regularization is among the most powerful an...
In this paper, we investigate the relationship between deep neural netwo...
We study ReLU deep neural networks (DNNs) by investigating their connect...
In this paper, we develop a new neural network family based on power ser...
In this paper, we propose a constrained linear data-feature mapping mode...
We develop a unified model, known as MgNet, that simultaneously recovers...
We proposed a modified regularized dual averaging method for training sp...