5 papers
Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks
Junyu Zhou, Puyu Wang, Yunwen Lei +2
Recent progress has been made in understanding the statistical generalization performance of gradient descent methods for overparameterized neural networks within the neural tangen…
Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension
Jianfei Li, Shuo Huang, Han Feng +2
Deep neural networks have emerged as powerful tools for learning operators defined over infinite-dimensional function spaces. However, existing theories frequently encounter diffic…
Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks
Jianfei Li, Han Feng, Ding-Xuan Zhou
In this work, we explore the intersection of sparse coding theory and deep learning to enhance our understanding of feature extraction capabilities in advanced neural network archi…
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…
Nonlinear functional regression by functional deep neural network with kernel embedding
Zhongjie Shi, Jun Fan, Linhao Song +2
Recently, deep learning has been widely applied in functional data analysis (FDA) with notable empirical success. However, the infinite dimensionality of functional data necessitat…