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Showing 1 - 20 results of 59 for search '(( element te algorithm ) OR ((( elements making algorithm ) OR ( neural codingn_ algorithm ))))', query time: 0.50s Refine Results
  1. 1
    TIR-Learner v3: New generation TE annotation program for identifying TIRs

    TIR-Learner v3: New generation TE annotation program for identifying TIRs by Tianyu Lu (18856792)

    Published 2025
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  2. 2
    Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements.

    Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements. by Martina Bukač (20161460)

    Published 2024
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  3. 3
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  4. 4
    Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets.

    Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. by Tomokaze Shiratori (9635271)

    Published 2024
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  5. 5
    Block diagram of the SSNMF algorithm for source separation.

    Block diagram of the SSNMF algorithm for source separation. by Balamurali Krishna Ponukumati (19864775)

    Published 2024
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  6. 6
    Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40).

    Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40). by Johannes Weyer (13392175)

    Published 2025
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  7. 7
    Computation time as a function of the sample size on the chain graph dataset.

    Computation time as a function of the sample size on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  8. 8
    Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges).

    Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

    Published 2024
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  9. 9
    Computation time as a function of the sample size on the random graph dataset.

    Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  10. 10
    F1 score of edges selected through cross-validation on the chain graph dataset.

    F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  11. 11
    Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges).

    Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

    Published 2024
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  12. 12
    Examples of ground-truth graph structures with (<i>p</i>, <i>n</i><sub>≠0</sub>) = (10, 10).

    Examples of ground-truth graph structures with (<i>p</i>, <i>n</i><sub>≠0</sub>) = (10, 10). by Tomokaze Shiratori (9635271)

    Published 2024
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  13. 13
    Number of edges selected through cross-validation on the chain graph dataset.

    Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  14. 14
    Number of edges selected through cross-validation on the random graph dataset.

    Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  15. 15
    Graphs of the regularization terms.

    Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

    Published 2024
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  16. 16
    F1 score of a given number of selected edges on the chain graph dataset.

    F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  17. 17
    Computation time as a function of the number of variables on the chain graph dataset.

    Computation time as a function of the number of variables on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  18. 18
    Computation time as a function of the number of variables on the random graph dataset.

    Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  19. 19
    F1 score of a given number of selected edges on the random graph dataset.

    F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  20. 20
    F1 score of edges selected through cross-validation on the random graph dataset.

    F1 score of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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