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相关概念视频

Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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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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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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InitialGAN:一种具有完全随机初始化的语言 GAN.

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    此摘要是机器生成的。

    一个新的生成对抗网络 (GAN) InitialGAN,在没有预训练的情况下克服了文本生成中的曝光偏差. 这种方法通过解决现有方法的局限性来提高语言模型的性能.

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    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 机器学习

    背景情况:

    • 经过最大概率估计 (MLE) 训练的文本生成模型面临着显著的曝光偏差.
    • 生成对抗性网络 (GAN) 显示出减轻暴露偏差的希望,但通常需要预训练.
    • 由于历史性能问题,表示建模方法 (RMM) 的探索较少.

    研究的目的:

    • 开发一种语言GAN,克服暴露偏差,而不依赖于预训练技术.
    • 解决现有语言GANs中无效采样和不健康梯度的局限性.
    • 引入一个新的评估指标来评估生成文本的质量.

    主要方法:

    • 引入掉队抽样和完全正常化的长短期记忆 (LSTM) 网络.
    • 建议InitialGAN,一个具有随机初始化参数的GAN,利用这些技术.
    • 开发了一个新的评估指标,最小覆盖率 (LCR),用于生成的样本.

    主要成果:

    • 与MLE和其他现有模型相比,InitialGAN表现出更高的性能.
    • 提出的技术有效地解决了采样和RMM梯度方面的问题.
    • 在没有任何预训练的情况下,InitialGAN取得了最先进的结果.

    结论:

    • 最初的GAN成功地超过了MLE和其他语言的GAN,特别是在没有预训练的场景中.
    • 该研究强调了RMM在配备适当的采样和梯度处理时的潜力.
    • 本书介绍了第一个能够在没有预训练的情况下超越MLE的语言GAN.