• Corpus ID: 20204469

Tailoring Artificial Neural Networks for Optimal Learning

@article{Aceituno2017TailoringAN,
  title={Tailoring Artificial Neural Networks for Optimal Learning},
  author={Pau Vilimelis Aceituno and Yan Gang and Yang-Yu Liu},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.02469}
}
As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine to finance, and language processing. A key feature of the ESN paradigm is its reservoir ---a directed and weighted network--- that represents the connections between neurons and projects the input signals into a high dimensional space. Despite extensive studies, the impact of the reservoir network on the ESN performance remains… 

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This contribution discusses phenomena related to poor learning performance and suggests research directions to understand the reservoir dynamics in terms of a dynamical representation of the task's input signals.
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