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相关概念视频

Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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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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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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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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扰乱多样性证书强大的概括.

Zhuang Qian1, Shufei Zhang2, Kaizhu Huang3

  • 1Department of Electrical Engineering and Electronics, University of Liverpool, United Kingdom; School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China.

Neural networks : the official journal of the International Neural Network Society
|January 17, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的对抗性训练方法,该方法产生了多样化的对抗性示例,提高了深度神经网络的稳定性和概括性. 该方法通过创建更均的数据分布来缓解过度匹配,以加强对抗攻击的防御.

关键词:
对抗性的例子.敌对的强度 敌对的强度一个强大的概括.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 敌对训练对抗深层神经网络的敌对攻击是有效的,但患有过度拟合和糟糕的泛化.
  • 传统方法产生了偏见的对抗性例子,导致不均的数据分布和有限的稳定性.

研究的目的:

  • 提出一种用于生成多样化的对抗性示例的新方法,以提高强大的概括性.
  • 为了减轻传统的监督对抗训练的局限性.

主要方法:

  • 从扰乱多样性的角度生成对抗性示例,确保样本既具有对抗性又多样化.
  • 提供理论和经验分析来支持提出的方法.
  • 证明促进扰乱多样性可以改善强大的概括边界.

主要成果:

  • 拟议的方法产生多样化和对抗性样本,从而导致更均的数据分布.
  • 理论分析证实,扰动多样性增强了强大的概括界限.
  • 对CIFAR-10,CIFAR-100和SVHN数据集的广泛实验表明,与PGD和特征散射等最先进的方法相比,它们的性能优越.

结论:

  • 在深度神经网络中产生多样化的对抗性示例是实现强大的概括的关键.
  • 与现有方法相比,拟议的扰乱多样性方法在稳定性和通用性方面取得了显著的改进.
  • 这项工作为未来关于强有力的对抗训练的研究提供了坚实的理论和经验基础.