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Showing 1 - 20 results of 9,615 for search '(( based models algorithm ) OR ((( element making algorithm ) OR ( neural coding algorithm ))))', query time: 0.70s Refine Results
  1. 1
    Temporal response patterns determined by Dirichlet Process-based clustering algorithm across amplitudes and stimuli.

    Temporal response patterns determined by Dirichlet Process-based clustering algorithm across amplitudes and stimuli. by Dinh K. Tang (20469082)

    Published 2024
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  2. 2
    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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  3. 3
    Data and code resources.

    Data and code resources. by Sam Hall-McMaster (10343795)

    Published 2025
    “…Neither model-free perseveration or model-based control using a complete model of the environment could explain choice behavior. …”
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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
    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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  6. 6
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  7. 7
    The folder containing the code is provided as supportive documentation associated with this paper.

    The folder containing the code is provided as supportive documentation associated with this paper. by Quxi Kuang (22493270)

    Published 2025
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  8. 8
    IRS-assisted system model.

    IRS-assisted system model. by Sarmad Sohaib (21273402)

    Published 2025
    “…In contrast, the SBF algorithm adjusts the IRS elements using single-bit feedback. …”
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  9. 9
    Comparison of optical and silicon-based neural network implementations.

    Comparison of optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

    Published 2024
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  10. 10
    Comparison of power consumption in optical and silicon-based neural network implementations.

    Comparison of power consumption in optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

    Published 2024
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  11. 11
    Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks

    Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

    Published 2025
    “…<p dir="ltr">The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'</p><p dir="ltr">The data relates to 4 benchmarks:</p><ol><li>Spiking Heidelberg Digits (SHD).…”
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  12. 12
    Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code

    Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

    Published 2025
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  13. 13
    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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  14. 14
    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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  15. 15
    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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  16. 16
    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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  17. 17
    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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  18. 18
    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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  19. 19
    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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  20. 20
    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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