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Found 3 papers, 2 papers with code
Nys-Newton: Nyström-Approximated Curvature for Stochastic Optimization
However, the secant equation becomes insipid in approximating the Newton step owing to its use of the first-order derivatives.
FsNet: Feature Selection Network on High-dimensional Biological Data
Because a large number of parameters in the selection and reconstruction layers can easily result in overfitting under a limited number of samples, we use two tiny networks to predict the large, virtual weight matrices of the selection and reconstruction layers.
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method.
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