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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

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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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Uncertainty in Measurement: Accuracy and Precision03:37

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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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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: Overview00:59

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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 Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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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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不确定性校准测试时间模型的适应,而不忘记.

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    此摘要是机器生成的。

    有效的抗遗忘测试时间适应 (EATA) 和具有校准的EATA (EATA-C) 解决了机器学习中的分布变化. 这些方法可以降低计算成本,并在调整过程中防止原始数据的性能下降.

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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
    • 计算机视觉 计算机视觉

    背景情况:

    • 测试时间适应 (TTA) 对于面临数据分布变化的模型至关重要.
    • 现有的TTA方法带来了高的计算成本,并遭受了灾难性的遗忘,降低了原始数据的性能.
    • 当前的TTA方法往往导致过度自信的预测,低估数据不确定性.

    研究的目的:

    • 开发高效和有效的TTA方法,最大限度地降低计算成本和减轻灾难性遗忘.
    • 引入校准的TTA,准确地反映模型和数据不确定性.
    • 在动态测试环境中提高模型的稳定性和性能.

    主要方法:

    • 建议有效的抗遗忘测试时间调整 (EATA) 采用活跃的样本选择和费舍尔规范化.
    • 引入了EATA with Calibration (EATA-C),通过分离模型和数据不确定性来解决过度自信的预测问题.
    • EATA-C利用模型不确定性的网络分歧和数据不确定性的预测分歧,采用分歧最小化和最小-最大调整.

    主要成果:

    • 与以前的方法相比,EATA显著降低了优化成本.
    • 通过解决模型和数据不确定性,EATA-C有效校准预测.
    • 这两种方法在图像分类和语义细分任务中都表现出强的表现,优于现有的TTA解决方案.

    结论:

    • 对于测试时间的适应,EATA和EATA-C提供了高效和强大的解决方案.
    • 校准的TTA (EATA-C) 通过准确量化不确定性来提高预测可靠性.
    • 提出的方法在分布转移的情况下增强模型性能和概括性.