To Recognize Shapes First Learn to Generate Images. Hinton, G. E. In Computational Neuroscience: Theoretical Insights into Brain Function, 2007. Elsevier. Draft abstract bibtex The uniformity of the cortical architecture and the ability of functions to move to different areas of cortex following early damage strongly suggests that there is a single basic learning algorithm for extracting underlying structure from richly-structured, high-dimensional sensory data. There have been many attempts to design such an algorithm, but until recently they all suffered from serious computational weaknesses. This chapter describes several of the proposed algorithms and shows how they can be combined to produce hybrid methods that work efficiently in networks with many layers and millions of adaptive connections.
@InProceedings{ hinton2007:shapes,
abstract = {The uniformity of the cortical architecture and the
ability of functions to move to different areas of cortex
following early damage strongly suggests that there is a
single basic learning algorithm for extracting underlying
structure from richly-structured, high-dimensional sensory
data. There have been many attempts to design such an
algorithm, but until recently they all suffered from
serious computational weaknesses. This chapter describes
several of the proposed algorithms and shows how they can
be combined to produce hybrid methods that work efficiently
in networks with many layers and millions of adaptive
connections.},
added-at = {2009-06-02T18:51:39.000+0200},
author = {Hinton, Geoffrey E.},
biburl = {http://www.bibsonomy.org/bibtex/26f1036c425a1074ef9663d7169893381/rcreswick}
,
booktitle = {Computational Neuroscience: Theoretical Insights into
Brain Function},
citeulike-article-id={2599858},
interhash = {eddfdb0dfb34f6b81a06da1e5d1d0b16},
intrahash = {6f1036c425a1074ef9663d7169893381},
keywords = {learning},
posted-at = {2008-03-26 22:02:42},
priority = {2},
publisher = {Elsevier},
title = {To Recognize Shapes First Learn to Generate Images},
year = {2007},
url_draft = {absps/montrealTR.pdf}
}
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