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相关概念视频

Purposive Learning01:22

Purposive Learning

442
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
442
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

9.8K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
9.8K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

11.7K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
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Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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3.3K
Censoring Survival Data01:09

Censoring Survival Data

523
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
523
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

214
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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相关实验视频

Updated: Jan 15, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

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语法导向的快捷方式:一种语法级别的扰乱算法,用于防止文本数据被学习.

Bo Li, Kun Zhang, Xi Chen

    IEEE transactions on neural networks and learning systems
    |October 6, 2025
    PubMed
    概括
    此摘要是机器生成的。

    研究人员开发了一种新方法,使得大语言模型 (LLM) 无法学习文本数据. 该技术使用语法导向的快捷键来保护数据免受未经授权的训练,确保模型完整性.

    相关实验视频

    Last Updated: Jan 15, 2026

    Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
    05:33

    Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

    Published on: January 29, 2020

    6.4K

    科学领域:

    • 人工智能的人工智能
    • 自然语言处理自然语言处理.
    • 机器学习安全 机器学习安全

    背景情况:

    • 大型语言模型 (LLM) 在很大程度上依赖于大量的公开数据.
    • 人们对这些数据未经授权的用于LLM培训存在担忧.
    • 由于语义变化,现有的图像数据保护方法不适用于文本.

    研究的目的:

    • 提出一种新的算法,用于生成无法学习的文本示例.
    • 在LLM培训中保护文本数据免受未经授权的使用.
    • 为了解决将基于图像的扰动方法应用于文本的局限性.

    主要方法:

    • 通过语法导向快捷方式 (UTE-SS) 开发了不可学习的文本示例生成算法.
    • 引入了一个语法模板生成器 (STG) 以实现最佳类别特定语法扰动.
    • 设计了一个扰乱文本生成器 (PTG) 用于使用语法模板来修改文本,创建不可察觉但有效的偏差.

    主要成果:

    • UTE-SS算法成功生成了无法学习的文本示例.
    • 模型被误导学习语法类别的快捷方式,防止信息挖掘.
    • 在八个基于变压器的预训练语言模型 (PLM) 和四个NLP任务中证明了有效性和灵活性.

    结论:

    • 拟议的UTE-SS方法为保护文本数据免受未经授权的LLM培训提供了有效的解决方案.
    • 以语法为导向的方法克服了扰乱离散文本数据的挑战.
    • 该算法易于实现,并且具有公开可用的代码.