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相关概念视频

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.8K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.8K
Multiple Bar Graph01:07

Multiple Bar Graph

5.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.0K
Signal Flow Graphs01:18

Signal Flow Graphs

154
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
154
Bar Graph01:07

Bar Graph

15.9K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
15.9K
Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Skewness01:06

Skewness

10.9K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
10.9K

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相关实验视频

Updated: May 24, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K

GNNFairViz:用于图形神经网络公平性的视觉分析

Xinwu Ye, Jielin Feng, Erasmo Purificato

    IEEE transactions on visualization and computer graphics
    |March 4, 2025
    PubMed
    概括

    图形神经网络 (GNN) 可能不公平. 我们介绍了GNNFairViz,这是一个视觉分析工具,可以帮助开发人员检测和减轻GNN模型中的偏差,确保在敏感应用程序中获得更公平的结果.

    科学领域:

    • 人工智能的人工智能
    • 数据科学数据科学数据科学
    • 人与计算机的交互

    背景情况:

    • 图形神经网络 (GNN) 显示出巨大的潜力,但引发了公平性问题,特别是在以人为中心的应用程序中,冒着歧视的风险.
    • 现有的用于机器学习 (ML) 公平性的视觉分析通常忽视了GNN所带来的独特挑战.
    • 在GNN中的属性和结构偏差可以导致显著的模型偏差,需要专门的分析工具.

    研究的目的:

    • 提出一种新的视觉分析框架,用于分析和减轻图形神经网络 (GNN) 中的公平性问题.
    • 提供关于属性和结构偏差如何导致GNN中的模型偏差的见解.
    • 为GNN开发人员开发一个操作工具,GNNFairViz,以主动评估和解决公平性问题.

    主要方法:

    • 开发了GNN公平性分析的模型不可知视觉分析框架,支持多个敏感属性.
    • 创建了GNNFairViz,这是一个集成到GNN开发工作流程中的交互式视觉分析工具.
    • 利用一套扩展的公平度指标套件进行全面的偏见检查和诊断.

    主要成果:

    • GNNFairViz使开发人员能够有效地分析GNN偏差,选择节点并执行公平性检查.
    • 通过使用场景和专家采访进行评估,证实了该框架的有效性和可用性.

    更多相关视频

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    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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    Revealing Neural Circuit Topography in Multi-Color
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    Revealing Neural Circuit Topography in Multi-Color

    Published on: November 14, 2011

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    相关实验视频

    Last Updated: May 24, 2025

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.2K
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

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    Revealing Neural Circuit Topography in Multi-Color
    09:11

    Revealing Neural Circuit Topography in Multi-Color

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  • 确定了不平衡数据集中的"压倒性效应",并强调了GNN架构在缓解偏差方面的作用.
  • 结论:

    • 拟议的视觉分析框架和GNNFairViz工具显著提高了分析和解决GNN公平性的能力.
    • 这些发现为开发更公平的GNN模型在现实世界的应用提供了实际指导.
    • 对GNN公平性的进一步研究应考虑数据集不平衡和有效减轻偏差的架构选择.