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61
Explained variance ration of the PCA algorithm.
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. …”
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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 .
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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Recombinant mapping narrows region of interest to identify a loss-of-function allele of <i>mttp.</i>
Published 2025Subjects: -
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Wav2DDK: An automated DDK estimation algorithm (Kadambi et al., 2023)
Published 2023“…<p><strong>Purpose:</strong> Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. …”
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66
Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics
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.
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
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 .
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. …”
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76
Rosenbrock function losses for .
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. …”
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Levy function losses for .
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. …”
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78
Rastrigin function losses for .
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. …”
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Levy function losses for .
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. …”
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80
Rastrigin function losses for .
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. …”