G. S. Vruthula Shruthi

Abstract Instance-wise feature selection aims at discovering the most relevant set of attributes for each particular sample, providing a natural direction for achieving adaptive learning on inputs of heterogeneous and high-dimensional nature. While recent neural-net models made impressive advances, all current methods share a common drawback: the selected feature mask is considered as a definite one.