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相关概念视频

What is a Mode?01:07

What is a Mode?

19.3K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

132
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
132
Skewness01:06

Skewness

11.8K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
11.8K
Mean Absolute Deviation01:13

Mean Absolute Deviation

2.7K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.7K
Associative Learning01:27

Associative Learning

450
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
450
Weighted Mean00:57

Weighted Mean

5.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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相关实验视频

Updated: Jul 23, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Published on: June 13, 2025

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通过度量学习实现多模式数据的对抗性稳定性.

Sarwar Khan1,2,3, Jun-Cheng Chen1,2, Wen-Hung Liao2,3

  • 1Research Center for Information Technology Innovation, Academia Sinica, Taipei 11529, Taiwan.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种多原型的度量学习规范化,以改善深度神经网络的对抗性训练防御. 这种新的方法提高了对抗对手攻击的稳定性,而无需额外的计算成本.

关键词:
敌对的攻击是对抗性的攻击.进行对抗性培训.这是分类分类的分类.计量学学习学习的方法多种模式的多模式.它们是原型,原型.

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Last Updated: Jul 23, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Cross-Modal Multivariate Pattern Analysis
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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:

  • 深度神经网络面临来自对抗性攻击的重大安全威胁.
  • 现有的对抗防御方法在现实世界多模式数据集中经常失败,原因是单模式聚焦.
  • 目前的对抗性训练方法很难捕获全面的数据表示,以进行强大的防御.

研究的目的:

  • 为对抗性培训提出一种新的多原型度量学习规范化方案.
  • 为了增强对手训练对复杂攻击的防御能力.
  • 提高深度神经网络在复杂,多模式数据设置中的弹性.

主要方法:

  • 开发了一种多原型的度量学习规范化技术.
  • 将这种规范化整合到对抗性培训框架中.
  • 在各种数据集上进行了广泛的实验,包括CIFAR10,CIFAR100,MNIST和Tiny ImageNet.

主要成果:

  • 提出的方法显著提高了最先进的对抗训练技术的性能.
  • 与现有方法相比,在多原型数据集 (CIFAR10,CIFAR100) 上实现了优越的防御性能.
  • 通过防止对抗性示例潜伏表征的重大变化,证明了增强的稳定性.

结论:

  • 多原型度量学习规范化是对抗训练的有效增强.
  • 该方法提供了改进的防御能力,而不会增加计算开销.
  • 这种方法代表了防御深度神经网络免受敌对攻击的重大进步,特别是在多式联络场景中.