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Showing 161 - 180 results of 12,414 for search '(((( algorithm python function ) OR ( algorithm i function ))) OR ( algorithms mc function ))', query time: 1.28s Refine Results
  1. 161
    Choice behavior on individual test tasks.

    Choice behavior on individual test tasks. by Sam Hall-McMaster (10343795)

    Published 2025
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  2. 162
    Post scan feature estimates and confidence ratings.

    Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

    Published 2025
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  3. 163
    Subgroup analysis.

    Subgroup analysis. by Sam Hall-McMaster (10343795)

    Published 2025
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  4. 164
    Learning dynamics on training tasks.

    Learning dynamics on training tasks. by Sam Hall-McMaster (10343795)

    Published 2025
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  5. 165
    Associative memory paradigm used to train the feature decoders.

    Associative memory paradigm used to train the feature decoders. by Sam Hall-McMaster (10343795)

    Published 2025
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  6. 166
    Autocorrelation functions <i>C</i>(<i>r</i>, <i>t</i>) as functions of the rescaled distance <i>r</i>/<i>R</i>(<i>t</i>) for several time points, where <i>R</i>(<i>t</i>) is determined as the position of the first zero of the raw autocorrelation function at time <i>t</i>, shown in the inset.

    Autocorrelation functions <i>C</i>(<i>r</i>, <i>t</i>) as functions of the rescaled distance <i>r</i>/<i>R</i>(<i>t</i>) for several time points, where <i>R</i>(<i>t</i>) is determ... by Marc Durand (556746)

    Published 2021
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  7. 167
    Flowchart of one iteration of the CE-Symm algorithm.

    Flowchart of one iteration of the CE-Symm algorithm. by Spencer E. Bliven (6672755)

    Published 2019
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  8. 168
    Decoding evidence for feature triplets on test tasks.

    Decoding evidence for feature triplets on test tasks. by Sam Hall-McMaster (10343795)

    Published 2025
    “…This figure shows average decoding evidence for features associated with the more and less rewarding training policies on test trials (y-axis) as a function of brain region (x-axis). Feature information could not be decoded above chance in the four brain regions of interest (corrected <i>p-</i>values > 0.05). …”
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  9. 169
    Behavioral results.

    Behavioral results. by Sam Hall-McMaster (10343795)

    Published 2025
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  10. 170
    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. Decoding from functional magnetic resonance imaging data revealed that solutions from the SF&GPI algorithm were activated on test tasks in visual and prefrontal cortex. …”
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  11. 171
    Choices on individual test tasks were not explained by model-free perseveration.

    Choices on individual test tasks were not explained by model-free perseveration. by Sam Hall-McMaster (10343795)

    Published 2025
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  12. 172
    Decoding results.

    Decoding results. by Sam Hall-McMaster (10343795)

    Published 2025
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  13. 173
    Reward outcomes for each combination of task cue and policy in the experiment.

    Reward outcomes for each combination of task cue and policy in the experiment. by Sam Hall-McMaster (10343795)

    Published 2025
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  14. 174
    Validation tests of feature decoding during the associative memory paradigm.

    Validation tests of feature decoding during the associative memory paradigm. by Sam Hall-McMaster (10343795)

    Published 2025
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  15. 175
    Validation of the neural decoding pipeline.

    Validation of the neural decoding pipeline. by Sam Hall-McMaster (10343795)

    Published 2025
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  16. 176
    “trend+var” algorithm.

    “trend+var” algorithm. by Roman Olson (6577004)

    Published 2019
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  17. 177
    Distribution of DLCO in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians.

    Distribution of DLCO in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

    Published 2024
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  18. 178
    Function <i>χ</i><sub><i>opt</i></sub> superimposed with the phase-field function indicating the channel geometry.

    Function <i>χ</i><sub><i>opt</i></sub> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

    Published 2024
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  19. 179
    PathogenProteinFunctionalPredictionPSB2020 Poster

    PathogenProteinFunctionalPredictionPSB2020 Poster by Jason McDermott (413669)

    Published 2020
    “…<p>Large-scale Machine Learning and Graph Analytics for Functional Prediction of Pathogen Proteins</p><p> </p><p>Jason McDermott<sup>1,2</sup>, Song Feng<sup>1</sup>, William Nelson<sup>1</sup>, Joon-Yong Lee<sup>1</sup>, Sayan Ghosh<sup>1</sup>, Ariful Khan<sup>1</sup>, Mahantesh Halappanavar<sup>1</sup>, Justine Nguyen<sup>2</sup>, Jonathan Pruneda<sup>2</sup>, David Baltrus<sup>3</sup>, and Joshua Adkins<sup>1</sup></p><p> </p><p>1. …”
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  20. 180
    Detailed information of benchmark functions.

    Detailed information of benchmark functions. by Guangwei Liu (181992)

    Published 2024
    “…In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. …”
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