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A tutorial survey of architectures, algorithms, and applications for deep learning – ERRATUM

Published online by Cambridge University Press:  03 April 2014

Li Deng

Abstract

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Type
Erratum
Information
APSIPA Transactions on Signal and Information Processing , Volume 3 , 2014 , e5
Creative Commons
Creative Common License - CCCreative Common License - BY
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence http://creativecommons.org/licenses/by/3.0/
Copyright
Copyright © The Authors, 2014

In the above publication by Deng (Reference Deng2014), figure 2 has been mistakenly duplicated replacing figure 3. The publisher apologises for this error and the correct figure 3 is shown below.

Fig. 3. Top to bottom: Original spectrogram from the test set; reconstruction from the 312-bit VQ coder; reconstruction from the 312-bit auto-encoder; coding errors as a function of time for the VQ coder (blue) and auto-encoder (red); spectrogram of the VQ coder residual; spectrogram of the deep autoencoder's residual.

References

REFERENCE

[1] Deng, L (2014). A tutorial survey of architectures, algorithms, and applications for deep Learning. APSIPA Transactions on Signal and Information Processing, 3, e2. doi:10.1017/ATSIP.2013.9.Google Scholar
Figure 0

Fig. 3. Top to bottom: Original spectrogram from the test set; reconstruction from the 312-bit VQ coder; reconstruction from the 312-bit auto-encoder; coding errors as a function of time for the VQ coder (blue) and auto-encoder (red); spectrogram of the VQ coder residual; spectrogram of the deep autoencoder's residual.