1 citations · 1 across the 4 of their papers we have counts for
5 papers
Minimax rates for learning spectral Barron functions by deep ReLU neural networks
Songqiu Ma, Yunfei Yang
We study how well deep neural networks approximate and learn spectral Barron functions. Recent studies have shown that these function classes can be efficiently approximated by sha…
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
Yunfei Yang, Jun Fan
This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions. When the error is measured in norm and the approximator is a…
Memorization capacity of deep ReLU neural networks characterized by width and depth
Xin Yang, Yunfei Yang
This paper studies the memorization capacity of deep neural networks with ReLU activation. Specifically, we investigate the minimal size of such networks to memorize any data p…
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
Yunfei Yang
This paper studies the problem of how efficiently functions in the Sobolev spaces and Besov spaces can be approximated b…
On the rates of convergence for learning with convolutional neural networks
Yunfei Yang, Han Feng, Ding-Xuan Zhou
We study approximation and learning capacities of convolutional neural networks (CNNs) with one-side zero-padding and multiple channels. Our first result proves a new approximation…