Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models

18 Nov 2016Viktoriya KrakovnaFinale Doshi-Velez

As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks, state of the art models in speech recognition and translation... (read more)

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