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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

311
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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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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Uncertainty: Overview00:59

Uncertainty: Overview

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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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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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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相关实验视频

Updated: May 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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利用logit不确定性来获得更好的知识蒸.

Zhen Guo1,2, Dong Wang3, Qiang He3

  • 1Communication University of China, State Key Laboratory of Media Convergence and Communication, Beijing, 100024, China. cathy.guozhen@cuc.edu.cn.

Scientific reports
|December 29, 2024
PubMed
概括
此摘要是机器生成的。

逻辑不确定性蒸 (LUD) 通过专注于自信的教师预测和减少不确定性来增强知识蒸. 这种方法提高了学生模型的性能,即使有架构差异.

关键词:
知识的蒸知识的蒸.不确定性学习不确定性学习

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 知识蒸旨在将知识从一个更大的教师模型转移到一个更小的学生模型.
  • 由于架构和输出差异,更大的教师模型并不总是产生更好的蒸.
  • 教师模型对预测的信心对于有效的知识传递至关重要.

研究的目的:

  • 提出一种新的知识蒸方法,解决使用大型教师模型的局限性.
  • 通过有效地从教师模型转移知识来提高学生模型的表现.
  • 为了弥合不同架构的教师和学生模型之间的差距.

主要方法:

  • 建议使用Logits不确定性蒸 (LUD),并将类别不确定性权重纳入.
  • 使用信任值和面具来扣除蒸过程中不确定的类别.
  • 两个斯皮尔曼相关性损失函数在类别和样本级别对齐教师和学生模型逻辑.
  • 适应性动态温度因子优化了蒸过程.

主要成果:

  • 拟议的LUD方法提高了知识蒸的有效性.
  • 即使模型之间存在显著的架构差异,也可以实现有效的知识传输.
  • 跨多个数据集的实验验证实了该方法的性能.

结论:

  • 逻辑不确定性蒸 (LUD) 提供了一个强大的方法来提高学生的模型表现.
  • 该方法有效地处理教师预测中的不确定性,以便更好地传递知识.
  • LUD 促进了更高效,更准确的知识蒸,特别是在异质模型环境中.