3 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
Approximation in Contrastive Representation Learning
Yuanfan Li, Zihan Zhang, Yiming Ying +1
Contrastive representation learning (CRL) is a fundamental paradigm for learning transferable representations. Recent work identifies its population target as the pointwise mutual…
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…
Spherical Analysis of Learning Nonlinear Functionals
Zhenyu Yang, Shuo Huang, Han Feng +1
In recent years, there has been growing interest in the field of functional neural networks. They have been proposed and studied with the aim of approximating continuous functional…
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…
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…