In this paper, we propose a sparse and accelerated method for Sigma-Pi-Sigma neural network training based on smoothing group lasso regularization and adaptive ...

In this paper, we propose a sparse and accelerated method for Sigma-Pi-Sigma neural network training based on smoothing group lasso regularization and ...

Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network. https://doi.org/10.1016 ...

In this paper, we propose a sparse and accelerated method for Sigma-Pi-Sigma neural network training based on smoothing group lasso regularization and...... 小 ...

2022/10/27 · A key point of this paper is to modify the usual group lasso regularization term by smoothing it at the origin. The advantage of this processing ...

2024/06/26 · Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network. Inf ...

Kang, Q. Fan, J. M. Zurada, "Deterministic convergence analysis via smoothing ... sigma-pi-sigma neural network," Information Sciences, vol. 553, pp. 66-82 ...

In this article, we investigate the boundedness and convergence of the online gradient method with the smoothing group $L_{1/2}$ regularization for the sigma-pi ...

This work proves the deterministic convergence of the Sigma-Pi-Sigma neural network based on the batch gradient learning algorithm under certain relaxed ...

A pruning algorithm with relaxed conditions for high-order neural networks based on smoothing group L1/2 regularization and adaptive momentum.