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相关概念视频

Self-Schemas02:16

Self-Schemas

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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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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.
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...
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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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Schemata01:17

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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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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.
Classical conditioning, also known...
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相关实验视频

Updated: May 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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配对智能还是高阶智能? 一个自我适应的图表框架,用于知识图嵌入.

Dong Zhang1, Haoqian Jiang1, Xiaoning Li1

  • 1Information Science and Technology College, Dalian Maritime University, Dalian, 116026, Liaoning, China.

Neural networks : the official journal of the International Neural Network Society
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了PHGCN,这是一种知识图嵌入的新型模型,有效地集成了对智和高阶特征,克服了图形卷积网络中改善人工智能应用的常见挑战.

关键词:
图表 卷积网络 卷积网络知识图嵌入知识图.在光滑上进行过光滑.简化的复杂神经网络.

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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
  • 图形神经网络的神经网络

背景情况:

  • 知识图 (KG) 对人工智能至关重要,但存在不完整的问题.
  • 知识图嵌入 (KGE) 通过将实体和关系表示为向量来解决这个问题.
  • 图形卷积网络 (GCN) 是关键的KGE模型,但面临着过度平滑和有限的功能集成等挑战.

研究的目的:

  • 为了解决知识图表表示学习的GCN的局限性.
  • 开发一个有效集成对智和高阶特征的模型.
  • 提高知识图嵌入的准确性和性能.

主要方法:

  • 拟议的PHGCN (对智和高序图卷积网络),是一种自适应的GCN模型.
  • 利用层意识的GCN来减轻对智关系聚合中的过度平滑.
  • 使用简化的复杂神经网络来提取高阶拓特征.
  • 引入了一种自我适应的聚合机制,用于整合各种功能.

主要成果:

  • 在四个基准数据集上,PHGCN取得了最先进的结果.
  • 由于高阶特征提取,表现出显著的性能改进.
  • 在FB15k-237上实现了1.5%的改善,在WN18RR上实现了6.1%的改善.

结论:

  • PHGCN有效地克服了过度平滑,并整合了对智和高阶特征.
  • 该模型在知识图嵌入任务中提供了卓越的性能.
  • 强调高阶拓特征对于增强KG表示的重要性.