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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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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快速转移学习方法使用随机层结和特征精细化策略.

Wandong Zhang, Yimin Yang, Thangarajah Akilan

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    此摘要是机器生成的。

    本研究介绍了一种基于摩尔-罗斯反向 (MPI) 的诱导转移学习 (ITL) 在深 convolutional 神经网络 (DCNNs) 的快速再培训策略. 新方法显著加快了参数微调的收速度,使ITL变得更加实用.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 诱导转移学习 (ITL) 使用预训练的深卷积神经网络 (DCNNs).
    • 完全连接 (FC) 层的基于摩尔-罗斯逆 (MPI) 的参数微调是最近的ITL方法.
    • 目前基于MPI的ITL方法面临着由于高计算需求的挑战,这限制了实际应用.

    研究的目的:

    • 开发一种新的快速再培训策略,以提高基于MPI的ITL的适用性.
    • 解决现有的基于MPI的ITL方法的计算瓶.
    • 为了提高DCNN中参数微调的收速度.

    主要方法:

    • 在重新培训时期实施随机层结协议,以管理功能改进.
    • 纳入基于MPI的方法,在批处理过程中改进FC层参数.
    • 在ImageNet上评估了战略,预训练了基准DCNNs.

    主要成果:

    • 拟议的ITL策略与传统ITL方法相比,实现了竞争性表现.
    • 证明了显著改善的收速度.
    • 在ResNet-50上使用动量随随机梯度下降 (SGDM) 实现了接近1.5倍快于标准再训练的收.

    结论:

    • 新的快速再培训战略有效地提高了基于MPI的ITL的实用性.
    • 该方法为微调DCNN提供了一个计算效率高的替代方案.
    • 这种方法为加速深度学习模型适应提供了一个有希望的方向.