5 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
CauchyNet: Compact and Data-Efficient Learning using Holomorphic Activation Functions
Hong-Kun Zhang, Xin Li, Sikun Yang +1
A novel neural network inspired by Cauchy's integral formula, is proposed for function approximation tasks that include time series forecasting, missing data imputation, etc. Hence…
Enhancing Neural Function Approximation: The XNet Outperforming KAN
Xin Li, Xiaotao Zheng, Zhihong Xia
XNet is a single-layer neural network architecture that leverages Cauchy integral-based activation functions for high-order function approximation. Through theoretical analysis, we…
Complex Physics-Informed Neural Network
Chenhao Si, Ming Yan, Xin Li +1
We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optim…
Model Comparisons: XNet Outperforms KAN
Xin Li, Zhihong Jeff Xia, Xiaotao Zheng
In the fields of computational mathematics and artificial intelligence, the need for precise data modeling is crucial, especially for predictive machine learning tasks. This paper…
Cauchy activation function and XNet
Xin Li, Zhihong Xia, Hongkun Zhang
We have developed a novel activation function, named the Cauchy Activation Function. This function is derived from the Cauchy Integral Theorem in complex analysis and is specifical…