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Showing 1 - 20 results of 2,072 for search '(( element data algorithm ) OR ((( query processing algorithm ) OR ( level learning algorithm ))))*', query time: 0.38s Refine Results
  1. 1
    Example measurement and analysis units for landscape elements.

    Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

    Published 2025
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  2. 2
    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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  3. 3
    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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  4. 4
    City driving speed query results.

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

    Published 2025
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  5. 5
    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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  6. 6
    Description of the dataset used for the machine learning algorithm.

    Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)

    Published 2024
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  7. 7
    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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  8. 8
    The accuracy of all employed learning algorithms.

    The accuracy of all employed learning algorithms. by Quan Shao (20148209)

    Published 2024
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  9. 9
    Performance of different machine learning algorithms.

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

    Published 2024
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  10. 10
    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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  11. 11
    Framework of the machine learning algorithm for single-family housing price.

    Framework of the machine learning algorithm for single-family housing price. by Jaewon Han (8660295)

    Published 2025
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  12. 12
    Learning curves under various machine algorithms.

    Learning curves under various machine algorithms. by Jinlin Teng (17972595)

    Published 2024
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  13. 13
    Performance of ENQUIRE’s gene normalization algorithm.

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

    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
    Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025)

    Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025) by Brian Bockelman (5128082)

    Published 2025
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  16. 16
    Comparison results between the state-of-the-art algorithms and the proposed deep learning models for liquid level detection in terms of Accuracy.

    Comparison results between the state-of-the-art algorithms and the proposed deep learning models for liquid level detection in terms of Accuracy. by Dong Wang (73290)

    Published 2025
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  17. 17
    Trainable parameters for different algorithms.

    Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

    Published 2024
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  18. 18
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  19. 19
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  20. 20
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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