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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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对于归纳知识图嵌入的不确定性建模.

Chao Liu1, Sam Kwong2, Xizhao Wang3

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.

Neural networks : the official journal of the International Neural Network Society
|January 10, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了EDSU,这是一种新的感应知识图嵌入模型,它解决了实体特征的分布转移. EDSU有效地重建平均值和差异,以改善对不断变化的知识图表的表示学习.

关键词:
分布转移转移是分布转移的原因之一.嵌入空间 嵌入空间图形表示学习学习学习图形表示.诱导性链接预测的预测重建重建的重建工作

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科学领域:

  • 人工智能的人工智能
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 知识图 (KG) 经历了持续的改进,导致新兴实体和不断发展的现有实体.
  • 这种演变导致嵌入空间内的实体特征的分布转移,影响图形表示学习.
  • 现有的感应KG嵌入方法往往忽视了这些分布转移的不利影响.

研究的目的:

  • 开发一种新的感应知识图嵌入模型,EDSU,能够处理实体特征分布中的分布转移.
  • 通过整合实体内和实体间的特征来缓解分布转移引起的数据信息偏差.
  • 为理解分发转移处理作为分布式数据增强的一种形式提供一个框架.

主要方法:

  • 使用平均值和差异重建技术开发了EDSU模型.
  • 假设实体嵌入遵循一个多变量高斯分布.
  • 实体嵌入组件组件的组合分布特征与邻近结构信息以减轻数据偏差.

主要成果:

  • 与最先进的基线模型相比,EDSU模型显示出更高的性能.
  • 实验证实了EDSU在诱导链接预测任务中的有效性.
  • 该方法成功地缓解了实体内部和实体内部特征之间的数据信息偏差.

结论:

  • 埃德苏有效地解决了诱导知识图嵌入中分布转移的挑战.
  • 平均值和差异重建方法为处理不断变化的实体特征提供了强大的方法.
  • 这些发现表明了改善动态知识图中的表示学习的新方向.