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Showing 41 - 60 results of 312 for search 'data augmentation algorithm', query time: 0.23s Refine Results
  1. 41
    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors by Jialei Dai (13020462)

    Published 2024
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  2. 42
    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors by Jialei Dai (13020462)

    Published 2024
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  3. 43
    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors by Jialei Dai (13020462)

    Published 2024
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  4. 44
    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

    BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors by Jialei Dai (13020462)

    Published 2024
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  5. 45
    Augmentation effects on least accurate performance.

    Augmentation effects on least accurate performance. by John Kruper (18809386)

    Published 2025
    “…Almost none of the algorithms, across all augmentation levels, are as resilient to smaller training data as PCR Lasso (dashed line).…”
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  6. 46
    Raw data.

    Raw data. by Soheil Mohammadi (12468952)

    Published 2025
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  7. 47
    S1 Data -

    S1 Data - by Wenbing Shi (5806160)

    Published 2025
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  8. 48
    Comparison of clustering algorithms (K-Means, MeanShift, etc.) based on the average Silhouette Score.

    Comparison of clustering algorithms (K-Means, MeanShift, etc.) based on the average Silhouette Score. by Manju M. S. (22793350)

    Published 2025
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  9. 49
    The structure of the data sample.

    The structure of the data sample. by Wenbing Shi (5806160)

    Published 2025
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  10. 50
    Detailed characterization of the dataset.

    Detailed characterization of the dataset. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  11. 51
    Examples of EA selection rules (positive results).

    Examples of EA selection rules (positive results). by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  12. 52
    Examples of SA selection rules (negative results).

    Examples of SA selection rules (negative results). by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  13. 53
    Criteria for detailed characterization of the dataset.

    Criteria for detailed characterization of the dataset. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  14. 54
    Hyperparameters for each classification model.

    Hyperparameters for each classification model. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  15. 55
    SentiGAN configuration parameters.

    SentiGAN configuration parameters. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  16. 56
    Results for the baseline.

    Results for the baseline. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  17. 57
    Examples of EA selection rules (negative results).

    Examples of EA selection rules (negative results). by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  18. 58
    Example of back-translation based on Jacob and Shushma [8].

    Example of back-translation based on Jacob and Shushma [8]. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  19. 59
    Characterization of datasets.

    Characterization of datasets. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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  20. 60
    Method of work.

    Method of work. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
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