78 citations · 412 across the 48 of their papers we have counts for
5 papers · 1 filter
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…
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Puyu Wang, Yunwen Lei, Yiming Ying +1
Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving min…
Differentially Private Stochastic Gradient Descent with Low-Noise
Puyu Wang, Yunwen Lei, Yiming Ying +1
Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. Thi…
Attention Enables Zero Approximation Error
Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou +1
Deep learning models have been widely applied in various aspects of daily life. Many variant models based on deep learning structures have achieved even better performances. Attent…