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Neural Computation|June 7, 2008
Deep, narrow sigmoid belief networks are universal approximatorsIlya Sutskever, Geoffrey E HintonNeural Networks : the Official Journal of the International Neural Network Society|November 26, 2009
Temporal-kernel recurrent neural networksIlya Sutskever, Geoffrey HintonPhilosophical Transactions of the Royal Society of London. Series B, Biological Sciences|December 17, 2009
Learning to represent visual inputGeoffrey E HintonProgress in Brain Research|October 11, 2007
To recognize shapes, first learn to generate imagesGeoffrey E HintonNeural Computation|August 16, 2002
Training products of experts by minimizing contrastive divergenceGeoffrey E HintonTrends in Cognitive Sciences|October 9, 2007
Learning multiple layers of representationGeoffrey E HintonNeural Computation|February 10, 2010
Learning to represent spatial transformations with factored higher-order Boltzmann machinesRoland Memisevic, Geoffrey E HintonNeural Networks : the Official Journal of the International Neural Network Society|November 1, 1996
Varieties of Helmholtz MachineGeoffrey E. Hinton, Peter DayanNeural Computation|December 28, 2005
Topographic product models applied to natural scene statisticsSimon Osindero, Max Welling, Geoffrey E HintonNeural Computation|June 13, 2006
A fast learning algorithm for deep belief netsGeoffrey E Hinton, Simon Osindero, Yee-Whye TehPageof 2