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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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    此摘要是机器生成的。

    深度网络可能会超出噪音标签. 这项研究通过分解网络参数来解清洁和错误标记数据的记忆,从而改善噪音数据集的概括性能.

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    科学领域:

    • 机器学习 机器学习
    • 计算机科学 计算机科学

    背景情况:

    • 过度参数化的深度网络与杂的标签作斗争,导致不良的概括.
    • 记忆效应允许网络在噪音数据之前学习清洁数据,但早期停止无法区分它们.

    研究的目的:

    • 在深度网络中解清洁和错误标记数据的记忆.
    • 为了减少错误标记数据对模型概括的负面影响.

    主要方法:

    • 网络参数的增量分解成两个组:σ (清洁数据存储) 和γ (错误标记的数据存储).
    • 根据记忆效应调节s和g的更新,以优先考虑清洁的数据学习.
    • 在测试过程中仅使用 σ 参数来增强概括性.

    主要成果:

    • 拟议的方法有效地分离了清洁数据和噪音数据的学习.
    • 与现有方法相比,在模拟和现实世界的基准上表现出优越的性能.
    • 在有噪音标签的情况下显著提高了概括能力.

    结论:

    • 添加参数分解是一种可行的策略,用于打击深度学习中的噪音标签.
    • 该方法利用记忆效应来提高模型的稳定性和性能.
    • 这些发现为开发更有弹性的深度学习模型提供了新的方向.