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相关概念视频

Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Random Variables01:09

Random Variables

11.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.9K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

682
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...
682
Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
882
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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相关实验视频

Updated: Jul 2, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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随机性规范化与神经网络的简单一致性训练

Juntao Li, Xiaobo Liang, Lijun Wu

    IEEE transactions on pattern analysis and machine intelligence
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    此摘要是机器生成的。

    R-Drop是一种新的一致性训练策略,可以减少神经网络训练中因脱落随机性引起的不一致性. 这种方法通过确保在训练和推理过程中提供一致的输出,提高了各种任务和网络类型的模型性能.

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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:

    • 随机性,如掉队,通常用于神经网络训练,以帮助优化和防止过度拟合.
    • 然而,退出引入了培训和推理阶段之间的不一致性,可能会影响模型可靠性.

    研究的目的:

    • 引入R-Drop,一个简单的一致性训练策略,旨在规范神经网络中的随机性.
    • 解决基于脱学的规范化方法所造成的培训推理不一致问题.

    主要方法:

    • 对于每个训练样本,R-Drop 通过dropout 基于随机抽取的两个输出分布之间强制执行一致性.
    • 它最大限度地减少了这些输出分布之间的双向KL分歧.
    • 理论分析支持R-Drop能够减少子结构不一致性,并弥合完全和子模型损失之间的差距.

    主要成果:

    • 7个深度学习任务和23个数据集的实验表明R-Drop的普遍有效性.
    • 该方法在监督,参数效率和半监督学习下有利于前,反复和图形神经网络.
    • 在WMT14英语 → 德语和英语 → 法语翻译任务中,R-Drop使用 vanila Transformer 模型获得了最先进的结果.

    结论:

    • 在深度学习中,R-Drop提供了一种简单而有效的方法来规范随机性.
    • 它成功地减轻了培训-推理不一致性,从而提高了对具有挑战性的翻译任务的性能和最先进的结果.