Showing 1 - 10 results of 10 for search '(( significant ((teer decrease) OR (nn decrease)) ) OR ( significant application graphs ))', query time: 0.07s Refine Results
  1. 1

    Applying Graph Neural Networks to the Decision Version of Graph Combinatorial Optimization Problems by Raka Jovanovic (17947838)

    Published 2023
    “…<p dir="ltr">In recent years, there has been a significant increase in the application of graph neural networks on a wide range of different problems. …”
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    On the disk dimension of planar graphs by Abu-khzam, Faisal

    Published 2011
    “…Disk dimension finds application in circuit layout and related fields. For every fixed k, the family of ``yes'' instances is closed under minors and excludes a planar graph. …”
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    Toward RDF normalization by Ticona-Herrera, Regina

    Published 2017
    “…Experimental tests show significant improvements, namely in reducing RDF description loading time and file size.…”
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    Unsupervised knowledge representation of panoramic dental X-ray images using SVG image-and-object clustering by Salameh, Khouloud

    Published 2023
    “…Results underline our approach’s effectiveness, and its applicability in a practical application domain.…”
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    article
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    Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations by Mansour, Nashat

    Published 1992
    “…It produces a significant reduction in execution time, which is necessary for their applicability to large problems.…”
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    article
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    Enhancing fairness and scalability in IOTA tangle networks: a POMDP-based tip selection algorithm for decentralized systems by Mays Alshaikhli (22564811)

    Published 2025
    “…The Internet of Things Applications (IOTA) Tangle, a directed acyclic graph (DAG)-based structure, addresses these challenges by enabling scalable, feeless transactions for IoT applications. …”
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    Improving text-to-image generation with object layout guidance by Jezia Zakraoui (14151399)

    Published 2022
    “…We then introduce an object layout module to capture the features of these objects from the corresponding scene graph. Specifically, the object layout module aggregates individual object features from the scene graph as well as averaged or likelihood object features generated by a graph convolutional neural network. …”