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Geoffrey E. Hinton [7]Geoffrey Hinton [7]Ge Hinton [1]
  1.  37
    Lesioning an attractor network: Investigations of acquired dyslexia.Geoffrey E. Hinton & Tim Shallice - 1991 - Psychological Review 98 (1):74-95.
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  2.  56
    A Distributed Connectionist Production System.David S. Touretzky & Geoffrey E. Hinton - 1988 - Cognitive Science 12 (3):423-466.
    DCPS is a connectionist production system interpreter that uses distributed representations. As a connectionist model it consists of many simple, richly interconnected neuron‐like computing units that cooperate to solve problems in parallel. One motivation for constructing DCPS was to demonstrate that connectionist models are capable of representing and using explicit rules. A second motivation was to show how “coarse coding” or “distributed representations” can be used to construct a working memory that requires far fewer units than the number of different (...)
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  3.  45
    Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery.Geoffrey Hinton - 1979 - Cognitive Science 3 (3):231-250.
    A visual imagery task is presented which is beyond the limits of normal human ability, and some of the factors contributing to its difficulty are isolated by comparing the difficulty of related tasks. It is argued that complex objects are assigned hierarchical structural descriptions by being parsed into parts, each of which has its own local system of significant directions. Two quite different schemas for a wire‐frame cube are used to illustrate this theory, and some striking perceptual differences to which (...)
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  4.  23
    Connectionist learning procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
  5.  21
    Preface to the special issue on connectionist symbol processing.Geoffrey E. Hinton - 1990 - Artificial Intelligence 46 (1-2):1-4.
  6.  71
    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.  39
    Imagery without arrays.Geoffrey Hinton - 1979 - Behavioral and Brain Sciences 2 (4):555-556.
  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.  38
    Inferring the meaning of direct perception.Geoffrey E. Hinton - 1980 - Behavioral and Brain Sciences 3 (3):387-388.
  10.  42
    Three frames suffice.Geoffrey E. Hinton - 1985 - Behavioral and Brain Sciences 8 (2):296-297.
  11.  21
    The Unity of Consciousness: A Connectionist Account.Geoffrey E. Hinton - 1991 - In William Kessen, Andrew Ortony & Fergus I. M. Craik, Memories, Thoughts, and Emotions: Essays in Honor of George Mandler. Lawrence Erlbaum. pp. 245.
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  12.  31
    Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation.Geoffrey Hinton, Simon Osindero, Max Welling & Yee-Whye Teh - 2006 - Cognitive Science 30 (4):725-731.
    We describe a way of modeling high‐dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron‐like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of each unit depends on the activities in earlier layers are learned by minimizing the energy assigned to data vectors that are actually observed and maximizing the energy (...)
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