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相关概念视频

Hedgehog Signaling Pathway02:33

Hedgehog Signaling Pathway

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The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
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相关实验视频

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In situ Protocol for Butterfly Pupal Wings Using Riboprobes
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一点它飞:一个完全可变形的蝶网络.

Rui Lin, Jason Chun Lok Li, Jiajun Zhou

    IEEE transactions on neural networks and learning systems
    |November 28, 2023
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    概括

    这项研究引入了可变形蝶 (DeBut) 层,用于深度神经网络 (DNN) 压缩. DeBut层实现了极端的网络稀疏性和压缩,显著超过现有方法.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 深度神经网络 (DNN) 通常使用卷积和完全连接的层.
    • 网络压缩对于高效部署至关重要,传统方法包括修剪和低级分解.

    研究的目的:

    • 介绍和分析可变形蝶 (DeBut) 层作为DNN压缩的新方法.
    • 为了证明DNN同质化成DeBut层的有效性,以实现极端稀疏和压缩.

    主要方法:

    • 使用DeBut.But将过器矩阵分解为通用,类似蝶的因子.
    • 开发一个自动化的DeBut链生成器,以创建完全的DeBut网络.
    • 通过各种示例和硬件基准来评估网络性能.

    主要成果:

    • 揭示了DeBut和深度/点位卷积之间的密切联系,解释了DeBut的性能.
    • 通过将DNN同质化成全DeBut层来实现极端稀疏性和压缩.
    • 将PointNet压缩到其原始参数的5%以下,精度损失最小 (<5%).

    结论:

    • DeBut层提供了一个与传统方法直角的压缩策略.

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  • 所有DeBut网络在稀疏性和参数减少方面具有显著的优势.
  • 这种方法为DNN压缩创造了新的纪录,特别是对于PointNets.