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Geoffrey E. Hinton [7]Geoffrey Hinton [5]G. Hinton [2]G. E. Hinton [1]
  1.  40
    A Learning Algorithm for Boltzmann Machines.David H. Ackley, Geoffrey E. Hinton & Terrence J. Sejnowski - 1985 - Cognitive Science 9 (1):147-169.
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  2.  16
    Lesioning an Attractor Network: Investigations of Acquired Dyslexia.Geoffrey E. Hinton & Tim Shallice - 1991 - Psychological Review 98 (1):74-95.
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  3.  15
    A Distributed Connectionist Production System.David S. Touretzky & Geoffrey E. Hinton - 1988 - Cognitive Science 12 (3):423-466.
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  4. Learning Multiple Layers of Representation.Geoffrey E. Hinton - 2007 - Trends in Cognitive Sciences 11 (10):428-434.
  5.  17
    Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery.Geoffrey Hinton - 1979 - Cognitive Science 3 (3):231-250.
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  6.  50
    Where Do Features Come From?Geoffrey Hinton - 2014 - Cognitive Science 38 (6):1078-1101.
    It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One (...)
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  7.  7
    Scene-Based and Viewer-Centered Representations for Comparing Shapes.G. Hinton - 1988 - Cognition 30 (1):1-35.
  8. Discovering Binary Codes for Documents by Learning Deep Generative Models.Geoffrey Hinton & Ruslan Salakhutdinov - 2011 - Topics in Cognitive Science 3 (1):74-91.
    We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. We present efficient learning and inference procedures for this type of generative model and show that it allows more accurate and much faster retrieval than latent (...)
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  9.  16
    Imagery Without Arrays.Geoffrey Hinton - 1979 - Behavioral and Brain Sciences 2 (4):555-556.
  10.  19
    Is Achieved. Prior to Stabilization, Neural Networks Do Not Jump Around Between Points in Activation Space. Stabiliza-Tion is the Process Whereby a Network First Generates a de-Terminate Activation Pattern, and Thereby Arrives at a Point in Activation Space. [REVIEW]D. E. Rumelhart, P. Smolensky, J. L. McClelland & G. E. Hinton - 2004 - Behavioral and Brain Sciences 27:2.
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  11.  15
    Embden, G. 271 Engels, E 57 (N. 11).R. M. Evans, R. Galambos, N. Geschwind, K. Grelling, K. Gunderson, L. Hartshorn, W. Heisenberg, G. Hinton, G. H. Hogeboom & P. Hoyningen-Huene - 1992 - In Ansgar Beckermann, H. Flohr & Jaegwon Kim (eds.), Emergence or Reduction?: Essays on the Prospects of Nonreductive Physicalism. W. De Gruyter.
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  12.  12
    Three Frames Suffice.Geoffrey E. Hinton - 1985 - Behavioral and Brain Sciences 8 (2):296-297.
  13.  13
    The Unity of Consciousness: A Connectionist Account.Geoffrey E. Hinton - 1991 - In William Kessen, Andrew Ortony & Fergus I. M. Craik (eds.), Memories, Thoughts, and Emotions: Essays in Honor of George Mandler. Lawrence Erlbaum. pp. 245.
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  14.  11
    Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation.Geoffrey Hinton, Simon Osindero, Max Welling & Yee-Whye Teh - 2006 - Cognitive Science 30 (4):725-731.
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  15.  7
    Inferring the Meaning of Direct Perception.Geoffrey E. Hinton - 1980 - Behavioral and Brain Sciences 3 (3):387-388.