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    NIPS
    1998
    146views Information Technology» more  NIPS 1998»
    Bayesian Modeling of Facial Similarity
    14 years 3 months ago
    Bayesian Modeling of Facial Similarity
    Download  www1.cs.columbia.edu
    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
    14 years 3 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
    14 years 3 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
    14 years 3 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
    14 years 3 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
    14 years 3 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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    NIPS
    1998
    102views Information Technology» more  NIPS 1998»
    Analyzing and Visualizing Single-Trial Event-Related Potentials
    14 years 3 months ago
    Analyzing and Visualizing Single-Trial Event-Related Potentials
    Download  papers.cnl.salk.edu
    Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield,...
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    NIPS
    1998
    164views Information Technology» more  NIPS 1998»
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
    14 years 3 months ago
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
    Download  www.cis.upenn.edu
    In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
    Michael J. Kearns, Satinder P. Singh
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    NIPS
    1998
    121views Information Technology» more  NIPS 1998»
    Inference in Multilayer Networks via Large Deviation Bounds
    14 years 3 months ago
    Inference in Multilayer Networks via Large Deviation Bounds
    Download  www.cis.upenn.edu
    We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks do...
    Michael J. Kearns, Lawrence K. Saul
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    NIPS
    1998
    95views Information Technology» more  NIPS 1998»
    Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
    14 years 3 months ago
    Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
    Download  www.cs.columbia.edu
    We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing ...
    Tony Jebara, Alex Pentland
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