Multidimensional Networks

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 385)

Abstract

Recurrent neural networks are an effective architecture for sequence learning tasks where the data is strongly correlated along a single axis. This axis typically corresponds to time, or in some cases (such as protein secondary structure prediction) one-dimensional space. Some of the properties that make RNNs suitable for sequence learning, such as robustness to input warping and the ability to learn which context to use, are also desirable in domains with more than one spatio-temporal dimension.

Keywords

Hide Layer Recurrent Neural Network Convolutional Neural Network Handwritten Digit Forward Pass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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