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相关概念视频

Distribution and Dispersion00:54

Distribution and Dispersion

20.8K
To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
20.8K
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

77.8K
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...
77.8K
Group Polarization01:01

Group Polarization

31.3K
Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
31.3K
Student t Distribution01:31

Student t Distribution

11.7K
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
11.7K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

1.4K
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
1.4K
Bias01:22

Bias

6.2K
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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相关实验视频

DDML:针对仇恨言论的多学生知识蒸

Ze Liu1, Zerui Shao1, Haizhou Wang1

  • 1School of Cyber Science and Engineering, Sichuan University, Chengdu 610211, China.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

检测在线仇恨言论对于心理健康和社会稳定至关重要. 一种新的深度蒸相互学习 (DDML) 方法改进了仇恨言论检测模型,使它们在多种语言中更加有效和准确.

关键词:
在 DDML 和 DDML 之间.仇恨言论检测 发现 仇恨言论检测知识的蒸知识的蒸.

相关实验视频

科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 计算社会科学 计算社会科学

背景情况:

  • 在线仇恨言论对用户的心理健康和社会稳定构成重大风险.
  • 网上仇恨言论越来越普遍,需要有效的检测方法.
  • 基于变压器的模型看起来很有前途,但在部署方面需要大量的计算.

研究的目的:

  • 开发一种高效有效的方法来检测仇恨言论.
  • 为了应对大型基于变压器的模型的计算挑战.
  • 通过一种新的知识蒸方法,提高仇恨言论检测系统的性能.

主要方法:

  • 建议深度蒸-相互学习 (DDML),一种知识蒸技术.
  • DDML利用一个教师网络和多个学生网络进行相互学习.
  • 使用DDML框架训练深度神经网络来检测仇恨言论.

主要成果:

  • 基于DDML的网络在各种数据集中表现出强的性能.
  • 该方法在10种语言和9个数据集上进行了测试.
  • 与基线模型相比,F1平均得分增加了4.87%.

结论:

  • DDML增强了用于检测仇恨言论的深度神经网络的性能.
  • 拟议的方法为部署先进模型提供了计算效率高的解决方案.
  • DDML显示了提高在线仇恨言论检测系统的准确性和效率的前景.