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One step backpropagation through time for learning input mapping in reservoir computing applied to speech recognition

Michiel Hermans (UGent) and Benjamin Schrauwen (UGent)
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Abstract
Recurrent neural networks are very powerful engines for processing information that is coded in time, however, many problems with common training algorithms, such as Backpropagation Through Time, remain. Because of this, another important learning setup known as Reservoir Computing has appeared in recent years, where one uses an essentially untrained network to perform computations. Though very successful in many applications, using a random network can be quite inefficient when considering the required number of neurons and the associated computational costs. In this paper we introduce a highly simplified version of Backpropagation Through Time by basically truncating the error backpropagation to one step back in time, and we combine this with the classic Reservoir Computing setup using an instantaneous linear readout. We apply this setup to a spoken digit recognition task and show it to give very good results for small networks.
Keywords
speech recognition, backpropagation, learning (artificial intelligence), recurrent neural nets, NEURAL-NETWORKS, SYSTEMS

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Chicago
Hermans, Michiel, and Benjamin Schrauwen. 2010. “One Step Backpropagation Through Time for Learning Input Mapping in Reservoir Computing Applied to Speech Recognition.” In IEEE International Symposium on Circuits and Systems, 521–524. New York, NY, USA: IEEE.
APA
Hermans, Michiel, & Schrauwen, B. (2010). One step backpropagation through time for learning input mapping in reservoir computing applied to speech recognition. IEEE International Symposium on Circuits and Systems (pp. 521–524). Presented at the 2010 IEEE International symposium on Circuits and Systems (ISCAS 2010) : Nano-bio circuit fabrics and systems, New York, NY, USA: IEEE.
Vancouver
1.
Hermans M, Schrauwen B. One step backpropagation through time for learning input mapping in reservoir computing applied to speech recognition. IEEE International Symposium on Circuits and Systems. New York, NY, USA: IEEE; 2010. p. 521–4.
MLA
Hermans, Michiel, and Benjamin Schrauwen. “One Step Backpropagation Through Time for Learning Input Mapping in Reservoir Computing Applied to Speech Recognition.” IEEE International Symposium on Circuits and Systems. New York, NY, USA: IEEE, 2010. 521–524. Print.
@inproceedings{1065982,
  abstract     = {Recurrent neural networks are very powerful engines for processing information that is coded in time, however, many problems with common training algorithms, such as Backpropagation Through Time, remain. Because of this, another important learning setup known as Reservoir Computing has appeared in recent years, where one uses an essentially untrained network to perform computations. Though very successful in many applications, using a random network can be quite inefficient when considering the required number of neurons and the associated computational costs. In this paper we introduce a highly simplified version of Backpropagation Through Time by basically truncating the error backpropagation to one step back in time, and we combine this with the classic Reservoir Computing setup using an instantaneous linear readout. We apply this setup to a spoken digit recognition task and show it to give very good results for small networks.},
  author       = {Hermans, Michiel and Schrauwen, Benjamin},
  booktitle    = {IEEE International Symposium on Circuits and Systems},
  isbn         = {9781424453092},
  issn         = {0271-4302},
  keyword      = {speech recognition,backpropagation,learning (artificial intelligence),recurrent neural nets,NEURAL-NETWORKS,SYSTEMS},
  language     = {eng},
  location     = {Paris, France},
  pages        = {521--524},
  publisher    = {IEEE},
  title        = {One step backpropagation through time for learning input mapping in reservoir computing applied to speech recognition},
  url          = {http://dx.doi.org/10.1109/ISCAS.2010.5537568},
  year         = {2010},
}

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