2 citations · 3 across the 8 of their papers we have counts for
9 papers
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…
Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks
Luwei Sun, Dongrui Shen, Feng Chuanwen +3
Motivated by challenges in conditional generative modeling, where the target conditional density takes the form of a ratio f1 over f2, this paper develops a theoretical framework f…
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
Guangrui Yang, Jianfei Li, Ming Li +2
In this paper, we explore the approximation theory of functions defined on graphs. Our study builds upon the approximation results derived from the -functional. We establish a t…
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…
Permutation Equivariant Graph Framelets for Heterophilous Graph Learning
Jianfei Li, Ruigang Zheng, Han Feng +2
The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network models and suggests aggregati…
SignReLU neural network and its approximation ability
Jianfei Li, Han Feng, Ding-Xuan Zhou
Deep neural networks (DNNs) have garnered significant attention in various fields of science and technology in recent years. Activation functions define how neurons in DNNs process…
Approximation analysis of CNNs from a feature extraction view
Jianfei Li, Han Feng, Ding-Xuan Zhou
Deep learning based on deep neural networks has been very successful in many practical applications, but it lacks enough theoretical understanding due to the network architectures…
Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks
Jianfei Li, Chaoyan Huang, Raymond Chan +3
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging. It i…
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets
Jianfei Li, Han Feng, Xiaosheng Zhuang
In this paper, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition. In particular, we construct a…