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    NIPS
    1997
    94views Information Technology» more  NIPS 1997»
    Reinforcement Learning with Hierarchies of Machines
    14 years 24 days ago
    Reinforcement Learning with Hierarchies of Machines
    Download  www.cs.berkeley.edu
    We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This ...
    Ronald Parr, Stuart J. Russell
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    NIPS
    1997
    96views Information Technology» more  NIPS 1997»
    Learning Generative Models with the Up-Propagation Algorithm
    14 years 24 days ago
    Learning Generative Models with the Up-Propagation Algorithm
    Download  hebb.mit.edu
    Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
    Jong-Hoon Oh, H. Sebastian Seung
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    NIPS
    1997
    91views Information Technology» more  NIPS 1997»
    A Hippocampal Model of Recognition Memory
    14 years 24 days ago
    A Hippocampal Model of Recognition Memory
    Download  compmem.princeton.edu
    A rich body of data exists showing that recollection of specific information makes an important contribution to recognition memory, which is distinct from the contribution of fam...
    Randall C. O'Reilly, Kenneth A. Norman, James L. M...
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    NIPS
    1997
    92views Information Technology» more  NIPS 1997»
    Learning to Schedule Straight-Line Code
    14 years 24 days ago
    Learning to Schedule Straight-Line Code
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    Program execution speed on modern computers is sensitive, by a factor of two or more, to the order in which instructions are presented to the processor. To realize potential execu...
    J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Do...
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    NIPS
    1997
    189views Information Technology» more  NIPS 1997»
    A Framework for Multiple-Instance Learning
    14 years 24 days ago
    A Framework for Multiple-Instance Learning
    Download  people.csail.mit.edu
    Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many ins...
    Oded Maron, Tomás Lozano-Pérez
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    NIPS
    1997
    87views Information Technology» more  NIPS 1997»
    Visual Navigation in a Robot Using Zig-Zag Behavior
    14 years 24 days ago
    Visual Navigation in a Robot Using Zig-Zag Behavior
    Download  www.iguana-robotics.com
    M. Anthony Lewis
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    NIPS
    1997
    57views Information Technology» more  NIPS 1997»
    Learning Nonlinear Overcomplete Representations for Efficient Coding
    14 years 24 days ago
    Learning Nonlinear Overcomplete Representations for Efficient Coding
    Download  papers.cnl.salk.edu
    Michael S. Lewicki, Terrence J. Sejnowski
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    NIPS
    1997
    77views Information Technology» more  NIPS 1997»
    Multi-modular Associative Memory
    14 years 24 days ago
    Multi-modular Associative Memory
    Download  www.cs.tau.ac.il
    Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multi-modu...
    Nir Levy, David Horn, Eytan Ruppin
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    NIPS
    1997
    91views Information Technology» more  NIPS 1997»
    A Neural Network Based Head Tracking System
    14 years 24 days ago
    A Neural Network Based Head Tracking System
    Download  hebb.mit.edu
    We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector a...
    Daniel D. Lee, H. Sebastian Seung
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    NIPS
    1997
    116views Information Technology» more  NIPS 1997»
    Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report
    14 years 24 days ago
    Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report
    Download  lsa.colorado.edu
    Singular value decomposition (SVD) can be viewed as a method for unsupervised training of a network that associates two classes of events reciprocally by linear connections throug...
    Thomas K. Landauer, Darrell Laham, Peter W. Foltz
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