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    NIPS
    2003
    144views Information Technology» more  NIPS 2003»
    Learning with Local and Global Consistency
    13 years 10 months ago
    Learning with Local and Global Consistency
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    We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
    Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
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    NIPS
    2003
    146views Information Technology» more  NIPS 2003»
    On the Concentration of Expectation and Approximate Inference in Layered Networks
    13 years 10 months ago
    On the Concentration of Expectation and Approximate Inference in Layered Networks
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    We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This frame...
    XuanLong Nguyen, Michael I. Jordan
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    NIPS
    2003
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    A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems
    13 years 10 months ago
    A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems
    Download  books.nips.cc
    Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circui...
    Rock Z. Shi, Timothy K. Horiuchi
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    NIPS
    2003
    128views Information Technology» more  NIPS 2003»
    Geometric Clustering Using the Information Bottleneck Method
    13 years 10 months ago
    Geometric Clustering Using the Information Bottleneck Method
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    We argue that K–means and deterministic annealing algorithms for geometric clustering can be derived from the more general Information Bottleneck approach. If we cluster the ide...
    Susanne Still, William Bialek, Léon Bottou
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    NIPS
    2003
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    Learning Near-Pareto-Optimal Conventions in Polynomial Time
    13 years 10 months ago
    Learning Near-Pareto-Optimal Conventions in Polynomial Time
    Download  www-2.cs.cmu.edu
    We study how to learn to play a Pareto-optimal strict Nash equilibrium when there exist multiple equilibria and agents may have different preferences among the equilibria. We focu...
    Xiao Feng Wang, Tuomas Sandholm
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