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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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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.
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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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.
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Self-Discrepancy Theory02:45

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One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
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相关实验视频

Updated: Jun 10, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

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为公平的图形表示而进行脱而出的对比学习.

Guixian Zhang1, Guan Yuan1, Debo Cheng2

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China; Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China; Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China.

Neural networks : the official journal of the International Neural Network Society
|October 10, 2024
PubMed
概括
此摘要是机器生成的。

图形神经网络 (GNN) 可以进行歧视. 公平脱的图形神经网络 (FDGNN) 框架使用数据增强和脱的对比学习来创建公平的节点表示,防止人工智能的偏见. 这种方法通过保护弱势群体来确保可信的人工智能.

关键词:
由因果关系启发的机器学习.公平的代表性学习学习公平的 公平的 公平的图表神经网络的神经网络

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形理论 图形理论

背景情况:

  • 图形神经网络 (GNN) 对于从图形结构数据中学习至关重要.
  • 由于敏感的属性,GNN预测可能会有偏见,导致歧视.
  • 迫切需要方法来确保GNN应用程序的公平性.

研究的目的:

  • 提出一个新的框架,公平解图形神经网络 (FDGNN),用于学习公平节点表示.
  • 解决 GNN 中的算法歧视问题,保护弱势群体.
  • 为建立更值得信赖的人工智能系统.

主要方法:

  • 通过增强,FDGNN增强了数据多样性,创建具有相同灵敏度但不同的图形结构的实例.
  • 反事实增强策略平衡了跨组的敏感属性分布.
  • 不纠的对比学习将敏感与非敏感的属性分开来进行公平的预测.

主要成果:

  • FDGNN在三个现实数据集的预测中表现出卓越的公平性.
  • 该框架有效地学习了脱而出的表示,最大限度地降低了敏感信息的影响.
  • 实验结果验证了与基线方法相比,FDGNN的疗效.

结论:

  • 在图形神经网络中实现公平性,FDGNN提供了一个强大的解决方案.
  • 解是一种有前途的技术,用于学习图形数据中的公平表示.
  • 该框架有助于开发值得信赖和公平的AI.