Výsledky vyhledávání - asynchronous stochastic approximation arguments

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  1. 1

    Distributed asynchronous consensus-based algorithm for blind calibration of sensor networks with autonomous gain correction Autor Stanković, Maja

    ISSN: 1751-8644, 1751-8652
    Vydáno: The Institution of Engineering and Technology 06.11.2018
    Vydáno v IET control theory & applications (06.11.2018)
    “… It is proved using asynchronous stochastic approximation arguments that the algorithm achieves asymptotic consensus with regard to both the corrected sensor gains and offsets in the mean square sense…”
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    Journal Article
  2. 2

    Learning Stationary Correlated Equilibria in Constrained General-Sum Stochastic Games Autor Hakami, Vesal, Dehghan, Mehdi

    ISSN: 2168-2267, 2168-2275
    Vydáno: United States IEEE 01.07.2016
    Vydáno v IEEE transactions on cybernetics (01.07.2016)
    “… A rigorous convergence analysis with differential inclusion arguments is given which draws on recent extensions of the theory of stochastic approximation to the case of asynchronous recursive…”
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    Journal Article
  3. 3

    Asynchronous Distributed Blind Calibration of Sensor Networks Under Noisy Measurements Autor Stankovic, Milos S., Stankovic, Srdjan S., Johansson, Karl Henrik

    ISSN: 2325-5870, 2372-2533
    Vydáno: IEEE 01.03.2018
    “… It is proved using asynchronous stochastic approximation arguments and properties of block-diagonally dominant matrices that the algorithm achieves asymptotic consensus for sensor gains and offsets…”
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  4. 4

    Asynchronous Stochastic Approximation with Differential Inclusions Autor Perkins, Steven, Leslie, David S.

    ISSN: 1946-5238, 1946-5238
    Vydáno: Institute for Operations Research and the Management Sciences (INFORMS) 01.12.2012
    Vydáno v Stochastic systems (01.12.2012)
    “…The asymptotic pseudo-trajectory approach to stochastic approximation of Benaïm, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field…”
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  5. 5

    Asynchronous Stochastic Approximation with Differential Inclusions Autor Perkins, Steven, Leslie, David S

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 10.12.2011
    Vydáno v arXiv.org (10.12.2011)
    “…The asymptotic pseudo-trajectory approach to stochastic approximation of Benaim, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field…”
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  6. 6

    Learning Stationary Correlated Equilibria in Constrained General-Sum Stochastic Games Autor Hakami, Vesal, Dehghan, Mehdi

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 01.06.2015
    Vydáno v arXiv.org (01.06.2015)
    “… A rigorous convergence analysis with differential inclusion arguments is given which draws on recent extensions of the theory of stochastic approximation to the case of asynchronous recursive…”
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  7. 7

    Long-tail buffer-content distributions in broadband networks Autor Choudhury, Gagan L., Whitt, Ward

    ISSN: 0166-5316, 1872-745X
    Vydáno: Elsevier B.V 01.09.1997
    Vydáno v Performance evaluation (01.09.1997)
    “… For this purpose, we analyze an infinite-capacity stochastic fluid model with a general stationary environment process…”
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  8. 8

    Spontaneous scale-free structure of spike flow graphs in recurrent neural networks Autor Piȩkniewski, Filip, Schreiber, Tomasz

    ISSN: 0893-6080, 1879-2782
    Vydáno: Kidlington Elsevier Ltd 01.12.2008
    Vydáno v Neural networks (01.12.2008)
    “…In this paper we introduce a simple and mathematically tractable model of an asynchronous spiking neural network which to some extent generalizes the concept of a Boltzmann machine…”
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  9. 9

    A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning Autor Mokhtari, Aryan, Koppel, Alec, Ribeiro, Alejandro

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 15.06.2016
    Vydáno v arXiv.org (15.06.2016)
    “… To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple parallel processors to operate on a randomly chosen subset of blocks of the feature vector…”
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