78 citations · 412 across the 48 of their papers we have counts for
48 papers
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
Guanhang Lei, Zhen Lei, Lei Shi +2
Physics-informed neural networks (PINNs) have been demonstrated to be efficient in solving partial differential equations (PDEs) from a variety of experimental perspectives. Some r…
Rates of Approximation by ReLU Shallow Neural Networks
Tong Mao, Ding-Xuan Zhou
Neural networks activated by the rectified linear unit (ReLU) play a central role in the recent development of deep learning. The topic of approximating functions from Hölder space…
Learning Theory of Distribution Regression with Neural Networks
Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou
In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classi…
Classification with Deep Neural Networks and Logistic Loss
Zihan Zhang, Lei Shi, Ding-Xuan Zhou
Deep neural networks (DNNs) trained with the logistic loss (i.e., the cross entropy loss) have made impressive advancements in various binary classification tasks. However, general…
Nonparametric regression using over-parameterized shallow ReLU neural networks
Yunfei Yang, Ding-Xuan Zhou
It is shown that over-parameterized neural networks can achieve minimax optimal rates of convergence (up to logarithmic factors) for learning functions from certain smooth function…
Fine-grained Analysis of Non-parametric Estimation for Pairwise Learning
Junyu Zhou, Shuo Huang, Han Feng +2
In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning. Most of the existing work requires the hypothesis space to b…