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    ICML
    2009
    IEEE
    124views Machine Learning» more  ICML 2009»
    Large-scale deep unsupervised learning using graphics processors
    14 years 8 months ago
    Large-scale deep unsupervised learning using graphics processors
    Download  ai.stanford.edu
    The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
    Rajat Raina, Anand Madhavan, Andrew Y. Ng
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    JGAA
    2006
    120views more  JGAA 2006»
    Distributing Unit Size Workload Packages in Heterogeneous Networks
    13 years 7 months ago
    Distributing Unit Size Workload Packages in Heterogeneous Networks
    Download  jgaa.info
    The task of balancing dynamically generated work load occurs in a wide range of parallel and distributed applications. Diffusion based schemes, which belong to the class of neares...
    Robert Elsässer, Burkhard Monien, Stefan Scha...
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    EOR
    2007
    104views more  EOR 2007»
    A cutting-plane approach for the two-dimensional orthogonal non-guillotine cutting problem
    13 years 7 months ago
    A cutting-plane approach for the two-dimensional orthogonal non-guillotine cutting problem
    Download  www.math.tu-dresden.de
    The two-dimensional orthogonal non-guillotine cutting problem (NGCP) appears in many industries (like wood and steel industries) and consists in cutting a rectangular master surfa...
    Roberto Baldacci, Marco A. Boschetti
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    IJHPCA
    2010
    105views more  IJHPCA 2010»
    A Pipelined Algorithm for Large, Irregular All-Gather Problems
    13 years 5 months ago
    A Pipelined Algorithm for Large, Irregular All-Gather Problems
    Download  www.mcs.anl.gov
    We describe and evaluate a new, pipelined algorithm for large, irregular all-gather problems. In the irregular all-gather problem each process in a set of processes contributes in...
    Jesper Larsson Träff, Andreas Ripke, Christia...
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    CSCW
    2012
    ACM
    228views Social Sciences» more  CSCW 2012»
    Shepherding the crowd yields better work
    12 years 3 months ago
    Shepherding the crowd yields better work
    Download  hci.stanford.edu
    Micro-task platforms provide massively parallel, ondemand labor. However, it can be difficult to reliably achieve high-quality work because online workers may behave irresponsibly...
    Steven Dow, Anand Pramod Kulkarni, Scott R. Klemme...
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