Jie Ding

most citedThe Efficacy of

L_{1}

Regularization in Two-Layer Neural Networks

4 citations · 4 across the 1 of their papers we have counts for

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cs.LG2023

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization

Gen Li, Ganghua Wang, Jie Ding

LASSO regularization is a popular regression tool to enhance the prediction accuracy of statistical models by performing variable selection through the $ℓ_{1}$ penalty, initially…

cs.LG2020★ 4 cited

The Efficacy of $L_{1}$ Regularization in Two-Layer Neural Networks

Gen Li, Yuantao Gu, Jie Ding

A crucial problem in neural networks is to select the most appropriate number of hidden neurons and obtain tight statistical risk bounds. In this work, we present a new perspective…

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization

The Efficacy of L1​ Regularization in Two-Layer Neural Networks

The Efficacy of $L_{1}$ Regularization in Two-Layer Neural Networks