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相关概念视频

Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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In- and Out-Groups01:31

In- and Out-Groups

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People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.
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Law of Independent Assortment02:03

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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
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Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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宿命:学习有效的二进制描述符与集团公平性.

Fan Zhang, Chong Chen, Xian-Sheng Hua

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    此摘要是机器生成的。

    本研究介绍了与专家混合的公平意识哈希 (FATE) 以解决图像检索深度哈希的伦理风险. FATE生成有歧视性和公正的二进制描述符,提高大规模数据检索中的公平性.

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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:

    • 深度哈希方法为大规模数据检索提供了计算效率.
    • 现有的深度哈希方法可能会带来伦理风险,特别是关于图像检索中的群体公平性.

    研究的目的:

    • 在学习哈希框架内调查和解决群体公平性问题.
    • 引入一种新的方法,即以专家混合 (FATE) 进行公平意识的哈希处理,用于无偏的二进制描述符生成.

    主要方法:

    • 在哈希网络中,FATE使用了专家混合框架,从不同角度进行汇总知识.
    • 结合公平意识的对比学习,使用敏感的标签和特征相似性.
    • 在深度特征和哈希代码上采用对抗式学习目标,以减轻群体偏见.

    主要成果:

    • 与最先进的方法相比,FATE在生成歧视性和公正的二进制描述符方面表现优越.
    • 该方法有效地提高了模型的能力,以公平有效地检索图像.
    • 对基准数据集的广泛实验验证了FATE的有效性.

    结论:

    • 在深度散列中,FATE成功地解决了群体公平性挑战,用于大规模的图像检索.
    • 拟议的方法提供了一个强大的解决方案,用于生成无偏见和歧视性的哈希代码.
    • FATE代表了人工智能驱动的检索系统在伦理考虑方面取得的重大进展.