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

Complementation Tests00:49

Complementation Tests

4.9K
A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...
4.9K
Probability Histograms01:17

Probability Histograms

11.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.1K
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K
Probability in Statistics01:14

Probability in Statistics

12.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
12.5K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
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...
5.1K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

119
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
119

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

Longitudinally altered default mode network and insula multimodal brain pattern in end-stage renal disease during sustained hemodialysis treatment.

iScience·2026
Same author

Spatial distribution of the proteome in the human body and in cancers.

Nature·2026
Same author

Major Depressive Disorder from a Brain-Body Perspective: Reproducible Central Cardiac Interoception Deficits and Peripheral Autonomic Dysfunctions Dissociate.

Biological psychiatry·2026
Same author

Sequence-specific radiomics for diagnosis of spinal bone loss.

Frontiers in endocrinology·2026
Same author

Microstructure and Properties of Crack-Free Ti-Modified 6063 Aluminum Alloy TPMS Porous Structures Fabricated by LPBF.

Materials (Basel, Switzerland)·2026
Same author

Effects of light therapy on depression, anxiety and sleep quality in mental disorders: A systematic review and meta-analysis.

General hospital psychiatry·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

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: Jun 18, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

概率图的补充对比学习学习.

Wenhao Jiang1, Yuebin Bai1

  • 1School of Computer Science and Engineering, Beihang University, Beijing, 100191, PR China.

Neural networks : the official journal of the International Neural Network Society
|August 3, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了概率图补充对比学习 (PGCCL),以提高图神经网络 (GNN) 在异性图上的性能. PGCCL通过自适应的方式构建补充图,通过捕获补充信息来改进表示学习.

关键词:
贝塔混合物模型模型预期最大化算法 预期最大化算法图表补充 图表补充图表对比学习学习的图表.异性恋是一种异性恋.

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

10.9K

相关实验视频

Last Updated: Jun 18, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

10.9K

科学领域:

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

背景情况:

  • 图形神经网络 (GNN) 在图形表示学习方面表现出色,但由于沿边缘的特征传播,与异构图形作斗争.
  • 像KNN图形这样的现有方法缺乏最佳参数选择,并面临不一致的相似性分布 (ISD) 问题.

研究的目的:

  • 建议概率图补充对比学习 (PGCCL) 用于自适应补充图构建.
  • 解决现有方法在处理异性图形特征方面的局限性.

主要方法:

  • 使用Beta混合模型 (BMM) 来区分类内和类间的相似性.
  • 构建基于后期概率的概率补充图,以创建对比的观点.
  • 利用对比式学习,在不同的观点中保存互补信息.

主要成果:

  • 拟议的PGCCL算法在20个不同的数据集中证明了有效性,包括同型和异型图.
  • 实验结果证实了与最先进的方法相比,概率补充图的优越质量.
  • 结合了原始和互补的图形嵌入,捕捉了丰富的语义,以改善微调.

结论:

  • 通过自适应地构建互补图,PGCCL有效地提高了在异构图上的GNN性能.
  • 该方法成功地减轻了与ISD和图形构造中的参数选择相关的问题.
  • 在复杂的图形结构中,PGCCL为图形表示学习提供了一个强大的方法.