Recurrent Online Clustering as a Spatio-Temporal Feature Extractor in DeSTIN

Steven R. Young, Itamar Arel

Jan 17, 2013 (modified: Jan 17, 2013) ICLR 2013 conference submission readers: everyone
  • Decision: reject
  • Abstract: This paper presents a basic enhancement to the DeSTIN deep learning architecture by replacing the explicitly calculated transition tables that are used to capture temporal features with a simpler, more scalable mechanism. This mechanism uses feedback of state information to cluster over a space comprised of both the spatial input and the current state. The resulting architecture achieves state-of-the-art results on the MNIST classification benchmark.

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