Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks

ArXi:2606.04404v1 Announce Type: cross The deep neural network is a widely used framework in machine learning that has been widely applied in various fields. However, deep neural networks often involve a large number of parameters and inputs, many of which may be irrelevant to the goal or true output. These parameters and \textcolor{black}{input variables} not only increase computational complexity, but also contribute to additional computational cost.