Capacity and Trainability in Recurrent Neural Networks

@article{Collins2016CapacityAT,
  title={Capacity and Trainability in Recurrent Neural Networks},
  author={Jasmine Collins and Jascha Sohl-Dickstein and David Sussillo},
  journal={CoRR},
  year={2016},
  volume={abs/1611.09913}
}
Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety of tasks and stacking depths. They can store an amount of task information which is linear in the number of parameters… CONTINUE READING
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