بدائل البحث:
processing algorithm » modeling algorithm (توسيع البحث), routing algorithm (توسيع البحث), tracking algorithm (توسيع البحث)
update algorithm » pass algorithm (توسيع البحث), data algorithms (توسيع البحث), ipca algorithm (توسيع البحث)
query processing » pre processing (توسيع البحث)
making algorithm » learning algorithm (توسيع البحث), finding algorithm (توسيع البحث), means algorithm (توسيع البحث)
element update » element data (توسيع البحث)
level making » level mapping (توسيع البحث), level mixing (توسيع البحث), level speaking (توسيع البحث)
يعرض 1 - 20 نتائج من 473 نتيجة بحث عن '(( element update algorithm ) OR ((( query processing algorithm ) OR ( level making algorithm ))))*', وقت الاستعلام: 0.46s تنقيح النتائج
  1. 1
    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... حسب Owen M. O’Connor (11965460)

    منشور في 2025
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  2. 2
    City driving speed query results.

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

    منشور في 2025
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  3. 3
    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... حسب Owen M. O’Connor (11965460)

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  4. 4
    Number of tweets collected per query and type.

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

    منشور في 2025
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  5. 5
    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... حسب Owen M. O’Connor (11965460)

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  6. 6
    Performance of ENQUIRE’s gene normalization algorithm.

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

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  7. 7
    Example of a knowledge query.

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

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  8. 8
    Comparative comparison between RSA algorithm, fuzzy RSA algorithm, algorithm.

    Comparative comparison between RSA algorithm, fuzzy RSA algorithm, algorithm. حسب Hanan Alolaiyan (18536236)

    منشور في 2024
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  9. 9
    Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer.

    Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. حسب Luca Musella (19986312)

    منشور في 2025
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  10. 10
    Features selection using the Boruta algorithm.

    Features selection using the Boruta algorithm. حسب Nishat Tasnim Thity (21755858)

    منشور في 2025
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  11. 11
    Performance of different machine learning algorithms.

    Performance of different machine learning algorithms. حسب Apichat Suratanee (634281)

    منشور في 2024
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  12. 12
    Decision-making model for location selection of tourism and leisure facilities.

    Decision-making model for location selection of tourism and leisure facilities. حسب Shifeng Wu (16612230)

    منشور في 2025
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  13. 13
    Execution of t-intuitionistic fuzzy RSA algorithm.

    Execution of t-intuitionistic fuzzy RSA algorithm. حسب Hanan Alolaiyan (18536236)

    منشور في 2024
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  14. 14
    The denoising results yielded by different algorithms on a dataset with Gaussian noise at a level of <i>σ</i> = 10<sup>−3</sup> after 30 iterations.

    The denoising results yielded by different algorithms on a dataset with Gaussian noise at a level of <i>σ</i> = 10<sup>−3</sup> after 30 iterations. حسب Limin Ma (556873)

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  15. 15
    The denoising results yielded by different algorithms on a dataset with Gaussian noise at a level of <i>σ</i> = 10<sup>−2</sup> after 30 iterations.

    The denoising results yielded by different algorithms on a dataset with Gaussian noise at a level of <i>σ</i> = 10<sup>−2</sup> after 30 iterations. حسب Limin Ma (556873)

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  16. 16
    An ordering level typology as price fluctuates at each level.

    An ordering level typology as price fluctuates at each level. حسب Minchao Zheng (20558832)

    منشور في 2025
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  17. 17
    Workflow of anonymous collaboration process.

    Workflow of anonymous collaboration process. حسب Zhaowei Hu (9234288)

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  18. 18
    The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri.

    The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri. حسب Burak Alakent (463512)

    منشور في 2025
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  19. 19
    MAE versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10<sup>−2</sup> and (b) <i>σ</i> = 10<sup>−3</sup>.

    MAE versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10<sup>−2</sup> and (b) <i>σ</i> = 10<sup>−3</sup>. حسب Limin Ma (556873)

    منشور في 2024
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  20. 20
    Mean prevalence of CoC across groups created using the k-means algorithm.

    Mean prevalence of CoC across groups created using the k-means algorithm. حسب Shafayet Khan Shafee (22593191)

    منشور في 2025
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