  Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method
for finding sparse solutions to ill-posed inverse problems. In this letter, we
present a data-driven scheme for learning optimal thresholding functions for
ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers
of a simple deep neural network (DNN) and developing a corresponding error
backpropagation algorithm that allows to fine-tune the thresholding functions.
Simulations on sparse statistical signals illustrate potential gains in
estimation quality due to the proposed data adaptive ISTA.
