Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Ogive Graph01:07

Ogive Graph

5.6K
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...
5.6K
Neural Circuits01:25

Neural Circuits

1.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.1K
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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Human telomerase reverse transcriptase (hTERT) promotes gastric cancer invasion through cooperating with c-Myc to upregulate heparanase expression.

Oncotarget·2015
Same author

Serum miR-21, miR-26a and miR-101 as potential biomarkers of hepatocellular carcinoma.

Clinics and research in hepatology and gastroenterology·2015
Same author

Brain tumor-targeted delivery and therapy by focused ultrasound introduced doxorubicin-loaded cationic liposomes.

Cancer chemotherapy and pharmacology·2015
Same author

[Establishment and characterization of a minipig model of microvascular coronary artery spasm].

Zhonghua xin xue guan bing za zhi·2015
Same author

Substrate Selectivity of Lysophospholipid Transporter LplT Involved in Membrane Phospholipid Remodeling in Escherichia coli.

The Journal of biological chemistry·2015
Same author

Three-dimensional verification of ¹²⁵I seed stability after permanent implantation in the parotid gland and periparotid region.

Radiation oncology (London, England)·2015

相关实验视频

Updated: Jun 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

475

ExGAT:背景扩展图表注意力神经网络神经网络

Pei Quan1, Lei Zheng2, Wen Zhang1

  • 1College of Economics and Management, Beijing University of Technology, Beijing, 100124, China.

Neural networks : the official journal of the International Neural Network Society
|October 20, 2024
PubMed
概括

本研究引入了基于注意力的增强图形神经网络 (GNN) 框架. 通过扩展图形上下文与多跳邻居,该模型改善了长距离依赖性捕获和整体性能.

关键词:
注意力机制注意力机制扩展的上下文 扩展的上下文图表注意力网络的图表.图形神经网络是一个神经网络.他们的PageRank是PageRank.

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.6K

相关实验视频

Last Updated: Jun 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

475
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.6K

科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 在注意力机制中,上下文至关重要,它定义了分析的范围.
  • 在基于注意力的GNN中,上下文是用于图形嵌入的节点集.
  • 使用直接邻居的现有方法限制了捕获远距离依赖关系.

研究的目的:

  • 提出一个新的基于注意力的GNN框架,具有扩展的背景.
  • 增强注意力机制捕捉长距离依赖关系在图表中的能力.
  • 通过优化上下文利用来提高GNN的性能.

主要方法:

  • 通过选择基于信息可转移性和跳数的多跳节点来扩展上下文.
  • 开发两个启发式上下文改进策略,以降低计算成本并保持本地图形结构.
  • 应用多头注意力对精细的,扩展的背景.

主要成果:

  • 拟议的方法在数值比较中显著优于23种基线方法.
  • 模型分析证实,与信息多跳邻居扩展上下文可以提高GNN的性能.
  • 精细的上下文策略有效地管理计算成本,同时保留基本的图形信息.

结论:

  • 基于注意力的新型GNN框架具有扩展的背景,有效地解决了传统方法的局限性.
  • 拟议的上下文扩展和精细化策略对于改善GNN中长途依赖性捕获至关重要.
  • 这种方法为图形表示学习提供了一种卓越的方法,特别是在复杂的图形结构中.