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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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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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功能噪音增强DNN 一般化 在标签噪音下

Lu Zeng, Xuan Chen, Xiaoshuang Shi

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    概括
    此摘要是机器生成的。

    将特征噪声 (FN) 添加到训练数据中,可以提高深度神经网络 (DNN) 的概括性,尽管有标签噪声. 这种方法通过限制概括界限来提高DNN性能,为强大的模型训练提供了一种新的方法.

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    科学领域:

    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 人工智能的人工智能

    背景情况:

    • 训练数据中的标签噪声显著降低了深度神经网络 (DNN) 的概括能力.
    • 现有的方法很难有效地减轻标签噪声对DNN性能的负面影响.

    研究的目的:

    • 引入和理论验证一种新的特征噪声 (FN) 方法,以增强标签噪声下的DNN概括.
    • 提供一个理论的理解,如何通过限制DNNs,FN提高了一般化.

    主要方法:

    • 直接将噪音注入训练数据的特征中.
    • 理论分析以证明FN如何影响概括界限和权重和特征之间的相互信息.
    • 定性分析以确定标签噪声场景的最佳FN策略.

    主要成果:

    • 理论分析证实,标签噪声通过宽松泛化界限来削弱DNN泛化.
    • 特性噪声 (FN) 通过对相互信息施加上限来改善DNN概括,从而限制了概括界限.
    • 广泛的实验表明,FN显著提高了流行数据集中最先进的方法的标签噪声概括.

    结论:

    • 拟议的特征噪声 (FN) 方法提供了一种简单而有效的方法,可以在标签噪声的存在下改善深度神经网络的泛化.
    • FN提供了一个理论上有基础的机制,用于增强对噪音标签的模型稳定性.