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

6.7K
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...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

52
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
52
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

543
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
543
Bar Graph01:07

Bar Graph

21.5K
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...
21.5K
Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K

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

Potentially functional variants of PLCE1 identified by GWASs contribute to gastric adenocarcinoma susceptibility in an eastern Chinese population.

PloS one·2012
Same author

Pennogenin tetraglycoside stimulates secretion-dependent activation of rat platelets: evidence for critical roles of adenosine diphosphate receptor signal pathways.

Thrombosis research·2012
Same author

Polymorphisms in the XPG gene and risk of gastric cancer in Chinese populations.

Human genetics·2012
Same author

[Influences of D-galactosamine and lipopolysaccharide on liver tissue regeneration and repair in mice with partial hepatectomy].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2012
Same author

Large and giant ventral paraclinoid carotid aneurysms: surgical techniques, complications and outcomes.

Clinical neurology and neurosurgery·2012
Same author

Association of mitochondrial DNA variations with lung cancer risk in a Han Chinese population from southwestern China.

PloS one·2012
Same journal

A boundary-regularization-enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

MT<sup>2</sup>-CSD and LLM-CRAN: A new dataset and an LLM-based multi-semantic knowledge fusion model for conversational stance detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TriAlignNet: A triple-path cross-modality alignment framework for multimodal time series forecasting.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jan 26, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.7K

UniTrain: 一个通用的代性的半监督培训框架,用于学习图形表示.

Xinlong Chen1, Jin Li2, Yisong Huang1

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.

Neural networks : the official journal of the International Neural Network Society
|January 24, 2026
PubMed
概括
此摘要是机器生成的。

全球代半监督训练 (UniTrain) 框架通过为未标记的节点生成高质量的伪标签来改善半监督学习中的图形神经网络和图形变压器,从而提高了节点分类任务的性能.

关键词:
图形神经网络是一个神经网络.图形变压器 图形变压器标签的提炼 标签的提炼半监督学习 半监督学习

更多相关视频

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

相关实验视频

Last Updated: Jan 26, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.7K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形表示学习学习学习图形表示学习

背景情况:

  • 图形神经网络 (GNN) 和图形变压器 (GT) 在与图形相关的任务中表现出色,但由于有限的标记数据,在半监督的设置中扎.
  • 尽管有强大的编码器和预训练,但过度装配和任务差距仍然存在,阻碍了GNN和GT的潜力.

研究的目的:

  • 引入通用代半监督培训 (UniTrain) 框架,以加强GNN和GT的半监督学习.
  • 解决标签稀缺问题,改善基于图形的机器学习中的节点分类性能.

主要方法:

  • UniTrain使用预训练阶段的隐藏向量表示构建了一个语义图.
  • 它使用标签知识传播与不确定性过来推断和完善未标签节点的标签.
  • 高可靠性伪标签被纳入以减轻噪音和补偿有限的原始标签指导.

主要成果:

  • 在七个不同的图表基准 (Cora,Citeseer,Pubmed,Actor,Cornell,德克萨斯州,威斯康星州) 中,UniTrain显著提高了节点分类性能.
  • 该框架在适用于GNN和GT时显示出显著的性能增长.
  • 实验结果验证了UniTrain方法的有效性和概括能力.

结论:

  • UniTrain有效地提高了对图形数据的现有自我监督学习方法的微调.
  • 该框架与任何GNN或GT编码器兼容,具有广泛的适用性.
  • UniTrain 提供了一个强大的解决方案,用于改善图表上的半监督学习,特别是在低标签的场景中.