  Instance-wise feature selection and ranking methods can achieve a good
selection of task-friendly features for each sample in the context of neural
networks. However, existing approaches that assume feature subsets to be
independent are imperfect when considering the dependency between features. To
address this limitation, we propose to incorporate the Gaussian copula, a
powerful mathematical technique for capturing correlations between variables,
into the current feature selection framework with no additional changes needed.
Experimental results on both synthetic and real datasets, in terms of
performance comparison and interpretability, demonstrate that our method is
capable of capturing meaningful correlations.
