Showing 61 - 80 results of 15,670 for search '(((( algorithm where functional ) OR ( algorithm a function ))) OR ( algorithm python function ))', query time: 0.70s Refine Results
  1. 61

    Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

    Published 2025
    “…We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. …”
  2. 62

    Simulation results for average reward rate function using the UCB algorithm , where <i>A</i> = 100, <i>ℓ</i> = 20, <i>μ</i> = [0.75, …(×50), 0.5, …(×50)], and <i>T</i> = 1000, and . by Justin Zobel (241587)

    Published 2022
    “…<p>Simulation results for average reward rate function using the UCB algorithm , where <i>A</i> = 100, <i>ℓ</i> = 20, <i>μ</i> = [0.75, …(×50), 0.5, …(×50)], and <i>T</i> = 1000, and .…”
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    Wav2DDK: An automated DDK estimation algorithm (Kadambi et al., 2023) by Prad Kadambi (16680635)

    Published 2023
    “…<p><strong>Purpose:</strong> Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. …”
  6. 66

    Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics by Rama K. Vasudevan (1612174)

    Published 2021
    “…Polarization dynamics in ferroelectric materials are explored <i>via</i> automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. …”
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    Performance of the three algorithms. by Juanjuan Lin (2096830)

    Published 2024
    “…An integrated framework based on a novel genetic algorithm and the Frank—Wolfe algorithm is designed to solve the stochastic model. …”
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    NLDock: a Fast Nucleic Acid–Ligand Docking Algorithm for Modeling RNA/DNA–Ligand Complexes by Yuyu Feng (11371729)

    Published 2021
    “…Here, we have developed a fast nucleic acid–ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid–ligand interactions into a modified version of the MDock program. …”
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    Rosenbrock function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
  16. 76

    Rosenbrock function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
  17. 77

    Levy function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
  18. 78

    Rastrigin function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
  19. 79

    Levy function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
  20. 80

    Rastrigin function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”