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相关概念视频

Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Associative Learning01:27

Associative Learning

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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...
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Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: May 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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通过有效利用服务器端的知识和客户端的不自信样本来促进半监督联合学习.

Hongquan Liu1, Yuxi Mi1, Yateng Tang2

  • 1Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai, China.

Neural networks : the official journal of the International Neural Network Society
|April 13, 2025
PubMed
概括

这项研究引入了一种新的半监督联合学习 (SSFL) 方法,以改善用有限的标记数据进行模型培训. 该方法有效地使用服务器知识和客户端数据,即使有不确定的标签,也优于现有技术.

关键词:
联合学习是联合学习.不同质的数据 不同质的数据半监督的联合学习.半监督学习 半监督学习不自信的伪标签使用.

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相关实验视频

Last Updated: May 13, 2025

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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 半监督联合学习 (SSFL) 训练具有较少标记数据的模型,当数据分散时至关重要.
  • 标签在服务器场景带来了挑战,因为非IID客户端数据和不可靠的伪标签可能会影响本地培训.
  • 现有的方法经常通过抛弃不确定的伪标签来低利用数据,并且在本地培训期间无法有效利用服务器知识.

研究的目的:

  • 通过利用服务器端标记数据和客户端未标记数据来提高SSFL的性能,包括带有不确定的伪标签的样本.
  • 为了解决非IID数据和错误的伪标签在服务器标签设置中带来的偏差.
  • 开发一种方法,提高模型准确性和融合速度,同时降低通信成本.

主要方法:

  • 提出了一个表示对齐模块,通过将本地特征与服务器端类代理对齐,以减轻非IID数据的影响.
  • 引入了收缩损失功能,以管理与不可靠的伪标签相关的风险,从而使更多客户端数据的使用成为可能.
  • 在各种非IID设置下对五个基准数据集进行了评估.

主要成果:

  • 拟议的方法在各种实验环境中明显优于现有的SSFL技术.
  • 该方法有效地减轻了源自非IID数据和伪标签不准确性的偏见.
  • 与以前的方法相比,表现出更好的性能和更快的趋同.
  • 实现了目标性能,降低了通信成本.

结论:

  • 新的SSFL方法有效地利用服务器和客户端数据,包括不确定的样本,以增强模型训练.
  • 代表性对齐和收缩损失组件是克服标签在服务器场景中的挑战的关键.
  • 该方法为SSFL提供了更高效和有效的方法,减少了对广泛标记数据集和通信开销的依赖.