Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery

22 Nov 2016 Scott Wisdom Thomas Powers James Pitton Les Atlas

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... (read more)

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