Improving the interpretability of deep neural networks with stimulated learning

@article{Tan2015ImprovingTI,
  title={Improving the interpretability of deep neural networks with stimulated learning},
  author={Shawn Tan and Khe Chai Sim and Mark J. F. Gales},
  journal={2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
  year={2015},
  pages={617-623}
}
Deep Neural Networks (DNNs) have demonstrated improvements in acoustic modelling for automatic speech recognition. However, they are often used as a black box, and not much is understood about what each of the hidden layers does. We seek to understand how the activations in the hidden layers change with different input, and how we can leverage such knowledge to modify the behaviour of the model. To this end, we propose stimulated deep learning where stimuli are introduced during the DNN… CONTINUE READING
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