AINeutralarXiv – CS AI · 6h ago6/10
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CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
Researchers introduce CatNet, an algorithm that controls False Discovery Rate (FDR) in LSTM neural networks by combining SHAP feature importance derivatives with a Gaussian Mirror statistical approach. The method addresses overfitting and model interpretability challenges in time-series deep learning through improved feature selection and a novel kernel-based independence measure.