Showing 1 - 16 results of 16 for search '(( significant decrease decrease ) OR ( significant ((linear decrease) OR (codes increased)) ))~', query time: 0.31s Refine Results
  1. 1

    The source code of LazyAct. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Scores vs Skip ratios on single-agent task. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Time(s) and GFLOPs savings of single-agent tasks. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Win rate vs Skip ratios on multi-agents tasks. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Visualization on SMAC-25m based on <i>LazyAct</i>. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Single agent and multi-agents tasks for <i>LazyAct</i>. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Network architectures for multi-agents task. by Hongjie Zhang (136127)

    Published 2025
    “…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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    Table 2_ILF-neurofeedback in clinical practice: examining symptom change and performance metrics across diagnostic groups.docx by Thomas Theis (1358547)

    Published 2025
    “…Symptoms significantly decreased during NF, with the fastest decline in the first 10 sessions. …”
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    Table 1_ILF-neurofeedback in clinical practice: examining symptom change and performance metrics across diagnostic groups.docx by Thomas Theis (1358547)

    Published 2025
    “…Symptoms significantly decreased during NF, with the fastest decline in the first 10 sessions. …”
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    Data Sheet 1_ILF-neurofeedback in clinical practice: examining symptom change and performance metrics across diagnostic groups.pdf by Thomas Theis (1358547)

    Published 2025
    “…Symptoms significantly decreased during NF, with the fastest decline in the first 10 sessions. …”
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    PFC neurons can encode the animal’s spatial position despite dHPC or vHPC silencing. by Susanne S. Babl (21169261)

    Published 2025
    “…<b>(F)</b> The average decoding error in light-off trials of each session (represented by a dot) decreases with increasing number of simultaneously recorded cells. …”
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    Supplementary Material for: Focus on the Blind Spot of Stone Disease: Analysis of Lower Urinary Tract Stone Interventions from 2006 to 2020 using German nationwide inpatient data. by Herout R. (20352174)

    Published 2024
    “…Methods We analyzed data from the nationwide German hospital billing database from 2006 to 2020. The significance of changes over time was evaluated via linear regression analysis. …”
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    Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

    Published 2025
    “…<br><br><i>1. </i><i>Linear Interpolation: </i>Missing values for all pollutants except PM₂.₅ (i.e., NO₂, SO₂, CO, PM₁₀, EC) were initially filled using standard linear interpolation (pandas.DataFrame.interpolate). …”