Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery

Understand

Recurrent neural networks (RNNs) are powerful and effective for processing sequential data.

• However, RNNs are usually considered "black box" models whose internal structure and learned parameters are not interpretable.
• In this paper, we propose an interpretable RNN based on the sequential iterative soft-thresholding algorithm (SISTA) for solving the sequential sparse recovery problem, which models a sequence of correlated observations with a sequence of sparse latent vectors.
• The architecture of the resulting SISTA-RNN is implicitly defined by the computational structure of SISTA, which results in a novel stacked RNN architecture.