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processing algorithm » routing algorithm (Expand Search), tracking algorithm (Expand Search), boosting algorithm (Expand Search)
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Showing 1 - 20 results of 3,532 for search '(( element based algorithm ) OR ((( query processing algorithm ) OR ( level modeling algorithm ))))', query time: 0.51s Refine Results
  1. 1
    The iterative process of genetic algorithm in the upper-level model.

    The iterative process of genetic algorithm in the upper-level model. by Zhipeng Huang (1759759)

    Published 2025
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  2. 2
    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is added to the track queries to get noised track queries. For Query Denoising (QD), random noise is added to the ground truth bounding boxes to generate noised track queries. All queries are processed together by the decoder. Attention masks are used to prevent information leakage between the object and track queries, the noised track queries generated from TGD, and the noised track queries generated from QD. The gray boxes indicate information being blocked between sets of queries while colored boxes indicate the flow of information.

    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

    Published 2025
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  3. 3
    City driving speed query results.

    City driving speed query results. by Meilin Zhu (688698)

    Published 2025
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  4. 4
    Cells are initially detected in frame t<sub>0</sub> and then tracked to frame t<sub>1</sub>. Object queries are processed in the first layer of the decoder for object detection. In the subsequent decoder layers, track queries are concatenated with the object queries to perform tracking.

    Cells are initially detected in frame t<sub>0</sub> and then tracked to frame t<sub>1</sub>. Object queries are processed in the first layer of the decoder for object detection. In... by Owen M. O’Connor (11965460)

    Published 2025
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  5. 5
    Number of tweets collected per query and type.

    Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

    Published 2025
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  6. 6
    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output alongside the output the from top matching object queries. Extracted from the self-attention mechanism in the decoder, the attention map highlights the strength of interactions among the queries. Lighter colors indicate stronger interactions. The track queries tend to focus on themselves, while the object queries attend to the track query that corresponds most closely in location.

    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output al... by Owen M. O’Connor (11965460)

    Published 2025
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  7. 7
    Algorithm testing.

    Algorithm testing. by Ziad M. Ali (12516532)

    Published 2024
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  8. 8
    BER comparison among PSM-based feedback algorithm, SBF algorithm, and perfect CSI using Monte-Carlo simulation.

    BER comparison among PSM-based feedback algorithm, SBF algorithm, and perfect CSI using Monte-Carlo simulation. by Sarmad Sohaib (21273402)

    Published 2025
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  9. 9
    The proposed model leverages different levels of analysis.

    The proposed model leverages different levels of analysis. by Daniel Schmid (1787929)

    Published 2025
    “…On the implementational level we provide an image-computable dynamical neural model that respects these complexity considerations and propose how a biological network of pyramidal cells and higher-order thalamic neurons could form interacting cortico-cortical and cortico-thalamo-cortical loops to implement this model.…”
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  10. 10
    Algorithm factor level change curve.

    Algorithm factor level change curve. by Kuiyuan Pan (21145338)

    Published 2025
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  11. 11
    Performance of ENQUIRE’s gene normalization algorithm.

    Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

    Published 2025
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  12. 12
    Algorithm for optimizing the model using transformer.

    Algorithm for optimizing the model using transformer. by Weiling Liu (1465480)

    Published 2025
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  13. 13
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  14. 14
    Example of a knowledge query.

    Example of a knowledge query. by Bei Li (90587)

    Published 2025
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  15. 15
    Convergence curves of the investigated algorithms.

    Convergence curves of the investigated algorithms. by Ziad M. Ali (12516532)

    Published 2024
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  16. 16
    Folder with all data and algorithms

    Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

    Published 2025
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  17. 17
    The model parameters of SIFusion and comparative algorithms.

    The model parameters of SIFusion and comparative algorithms. by Song Qian (5031221)

    Published 2024
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  18. 18
    ML algorithms in cyber security.

    ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

    Published 2025
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  19. 19
    Proposing sequence genetic distances with a locally greedy algorithm.

    Proposing sequence genetic distances with a locally greedy algorithm. by Hannah Waddel (21520023)

    Published 2025
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  20. 20
    Three-level model structure.

    Three-level model structure. by Zhanni Huang (20577481)

    Published 2025
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