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    NIPS
    1998
    79views Information Technology» more  NIPS 1998»
    Replicator Equations, Maximal Cliques, and Graph Isomorphism
    13 years 11 months ago
    Replicator Equations, Maximal Cliques, and Graph Isomorphism
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    Marcello Pelillo
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    NIPS
    1998
    137views Information Technology» more  NIPS 1998»
    Risk Sensitive Reinforcement Learning
    13 years 11 months ago
    Risk Sensitive Reinforcement Learning
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    In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
    Ralph Neuneier, Oliver Mihatsch
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    NIPS
    1998
    227views Information Technology» more  NIPS 1998»
    Controlling the Complexity of HMM Systems by Regularization
    13 years 11 months ago
    Controlling the Complexity of HMM Systems by Regularization
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    This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the E...
    Christoph Neukirchen, Gerhard Rigoll
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    NIPS
    1998
    88views Information Technology» more  NIPS 1998»
    Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts
    13 years 11 months ago
    Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts
    Download  www.cs.ou.edu
    The execution order of a block of computer instructions can make a difference in its running time by a factor of two or more. In order to achieve the best possible speed, compiler...
    Amy McGovern, J. Eliot B. Moss
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    NIPS
    1998
    146views Information Technology» more  NIPS 1998»
    Bayesian Modeling of Facial Similarity
    13 years 11 months ago
    Bayesian Modeling of Facial Similarity
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    In previous work 6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measur...
    Baback Moghaddam, Tony Jebara, Alex Pentland
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    NIPS
    1998
    112views Information Technology» more  NIPS 1998»
    Computational Differences between Asymmetrical and Symmetrical Networks
    13 years 11 months ago
    Computational Differences between Asymmetrical and Symmetrical Networks
    Download  www.gatsby.ucl.ac.uk
    Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, biological neural networks have asymmetrical connections, at...
    Zhaoping Li, Peter Dayan
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    NIPS
    1998
    147views Information Technology» more  NIPS 1998»
    Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
    13 years 11 months ago
    Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
    Download  papers.cnl.salk.edu
    A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
    Michael S. Lewicki, Terrence J. Sejnowski
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    NIPS
    1998
    132views Information Technology» more  NIPS 1998»
    Learning a Continuous Hidden Variable Model for Binary Data
    13 years 11 months ago
    Learning a Continuous Hidden Variable Model for Binary Data
    Download  web.mit.edu
    A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinearity distinguishes the model from conventional prin...
    Daniel D. Lee, Haim Sompolinsky
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    NIPS
    1998
    107views Information Technology» more  NIPS 1998»
    Exploring Unknown Environments with Real-Time Search or Reinforcement Learning
    13 years 11 months ago
    Exploring Unknown Environments with Real-Time Search or Reinforcement Learning
    Download  idm-lab.org
    Sven Koenig
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    NIPS
    1998
    106views Information Technology» more  NIPS 1998»
    A Polygonal Line Algorithm for Constructing Principal Curves
    13 years 11 months ago
    A Polygonal Line Algorithm for Constructing Principal Curves
    Download  www.iro.umontreal.ca
    Principal curves have been defined as "self consistent" smooth curves which pass through the "middle" of a d-dimensional probability distribution or data cloud...
    Balázs Kégl, Adam Krzyzak, Tam&aacut...
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