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Showing 1 - 12 results of 12 for search '(("sparse coding observed") OR ("source coding observed"))', query time: 0.26s Refine Results
  1. 1
    LESH performance on correlated data.

    LESH performance on correlated data. by Viktoria Zemliak (19500335)

    Published 2025
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  2. 2
    Connectivity adaptation.

    Connectivity adaptation. by Viktoria Zemliak (19500335)

    Published 2025
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  3. 3
    Plasticity regimes do not depend on repeat intervals.

    Plasticity regimes do not depend on repeat intervals. by Viktoria Zemliak (19500335)

    Published 2025
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  4. 4
    Model and experiments.

    Model and experiments. by Viktoria Zemliak (19500335)

    Published 2025
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  5. 5
    Predicting generalizability from connectivity.

    Predicting generalizability from connectivity. by Viktoria Zemliak (19500335)

    Published 2025
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  6. 6
    The genetic algorithm.

    The genetic algorithm. by Viktoria Zemliak (19500335)

    Published 2025
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  7. 7
    Plasticity regimes weakly depend on input similarity.

    Plasticity regimes weakly depend on input similarity. by Viktoria Zemliak (19500335)

    Published 2025
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  8. 8
    Jaccard similarity. Template similarity () and corresponding average pairwise Jaccard similarity computed from the generated dataset.

    Jaccard similarity. Template similarity () and corresponding average pairwise Jaccard similarity computed from the generated dataset. by Viktoria Zemliak (19500335)

    Published 2025
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  9. 9
    Models generalization across repeat intervals on the continual familiarity task.

    Models generalization across repeat intervals on the continual familiarity task. by Viktoria Zemliak (19500335)

    Published 2025
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  10. 10
    LESH performance on data of various sparseness.

    LESH performance on data of various sparseness. by Viktoria Zemliak (19500335)

    Published 2025
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  11. 11
    Distributions of optimal parameter values across sparseness levels.

    Distributions of optimal parameter values across sparseness levels. by Viktoria Zemliak (19500335)

    Published 2025
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  12. 12
    Stimulus sparseness. The proportion of 0s and 1s in the binary stimulus vector defines sparseness of the stimulus in a dataset.

    Stimulus sparseness. The proportion of 0s and 1s in the binary stimulus vector defines sparseness of the stimulus in a dataset. by Viktoria Zemliak (19500335)

    Published 2025
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