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相关概念视频

Types of Skewness01:09

Types of Skewness

11.4K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
11.4K
Normal Distribution01:11

Normal Distribution

10.6K
The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
10.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
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
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

192
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
192

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相关实验视频

Updated: Jun 8, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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没有负的自我监督的高斯嵌入图的图形.

Yunhui Liu1, Tieke He1, Tao Zheng1

  • 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China.

Neural networks : the official journal of the International Neural Network Society
|November 7, 2024
PubMed
概括

这项研究引入了一个新的无负值目标,用于图形对比学习 (GCL),以提高节点表示统一性. 新方法提高了没有负样本的统一性,降低了计算成本和内存使用量.

科学领域:

  • 机器学习 机器学习
  • 图形表示学习学习学习图形表示学习
  • 自主监督学习学习

背景情况:

  • 图形对比学习 (GCL) 使用对齐和统一的目标来学习没有标签的节点表示.
  • 现有的GCL方法严重依赖于负样本的统一性,导致高计算和内存需求.
  • 在GCL中采用负采样可以导致代表性崩和类碰撞问题.

研究的目的:

  • 为图形对比学习提出一个新的没有负的目标,以实现表示统一性.
  • 消除对负样本的依赖,从而减少计算和内存的开销.
  • 保持或提高GCL方法的性能,同时解决它们固有的局限性.

主要方法:

  • 引入了一个没有负的目标,其灵感来自于从正常化的同位素高斯方程中均分布的点数.
  • 尽量减少学习表示和同位方高斯分布之间的距离,以强制执行统一性.
  • 消除了对参数化的相互信息估计器,额外的投影仪和不对称的网络结构的需求.

主要成果:

  • 与现有的 GCL 方法相比,在七个图表基准上取得了竞争性表现.
  • 显著减少了计算需求,内存消耗和训练时间.
  • 在不使用负样本的情况下,成功地促进了节点表示的统一性.
关键词:
图形数据挖掘是指挖掘图形数据的过程.图形神经网络是一个神经网络.图形表示学习学习学习图形表示.自主监督学习学习

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结论:

  • 拟议的无负值目标有效地实现了图形对比学习中的表示统一性.
  • 这种方法为传统的GCL方法提供了更高效,更少资源密集的替代方案.
  • 这些发现表明了开发可扩展和有效的图形自主监督学习框架的新方向.