Two SVDs produce more focal deep learning representations

Hinrich Schuetze, Christian Scheible

Invalid Date (modified: Jan 17, 2013) ICLR 2013 conference submission readers: everyone
  • Decision: conferencePoster-iclr2013-workshop
  • Abstract: A key characteristic of work on deep learning and neural networks in general is that it relies on representations of the input that support generalization, robust inference, domain adaptation and other desirable functionalities. Much recent progress in the field has focused on efficient and effective methods for computing representations. In this paper, we propose an alternative method that is more efficient than prior work and produces representations that have a property we call focality -- a property we hypothesize to be important for neural network representations. The method consists of a simple application of two consecutive SVDs and is inspired by Anandkumar (2012).
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