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相关概念视频

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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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: 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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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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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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通过二进制分类探索神经网络中的不确定性原理.

Jun-Jie Zhang1, Jian-Nan Chen1, De-Yu Meng2,3

  • 1Northwest Institute of Nuclear Technology, Xi'an, 710024, Shaanxi, China.

Scientific reports
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概括
此摘要是机器生成的。

神经网络表现出准确性-稳定性权衡,更高的准确性增加了对敌对攻击的脆弱性. 这项研究使用量子力学原理来解释深度学习模型中这种固有的局限性.

关键词:
分类网络的分类网络.神经包是一个神经包.不确定性原则 不确定性原则

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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
  • 量子力学就是量子力学.

背景情况:

  • 神经网络容易受到对抗性攻击,其潜在机制尚未完全理解.
  • 现有的研究缺乏对这种脆弱性的定量衡量.

研究的目的:

  • 探索神经网络中准确性和稳定性之间的内在权衡.
  • 用量子力学为理解神经网络漏洞提供理论基础.
  • 为了揭示特征提取精度和对抗性扰动易感性之间的内在平衡.

主要方法:

  • 通过"不确定性原则"的镜头来框架准确性-稳定性权衡.
  • 应用量子力学的数学概念来分析神经网络行为.
  • 开发一种分析方法来量化漏洞.

主要成果:

  • 证明了一种固有的权衡:神经网络准确度的提高与对抗性攻击的脆弱性增加有关.
  • 确定了"不确定性关系"是这种现象的根本原因.
  • 提供了一个理论框架和分析工具,以了解深度学习模型的漏洞.

结论:

  • 该研究通过将其与基本原则联系起来,为神经网络安全提供了新的视角.
  • 这些发现表明,在同时实现高精度和稳定性方面存在固有的局限性.
  • 量子力学启发的方法为开发更安全的AI系统提供了新的途径.