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English(EN) Deep Residual Networks Learn the Geodesic Curve in the Wasserstein Space

深度残差网络学习Wasserstein空间中的测地线

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的arXiv论文提出，深度残差网络（ResNets）在训练过程中学习Wasserstein空间中的测地线。该研究使用连续性方程对ResNet前向传播进行建模，表明带有L2正则化的ResNets比普通网络更有效地逼近这条测地线。这种改进的逼近被认为是ResNets优化和泛化能力更好的原因。 AI

影响 这项研究为ResNets的优化和泛化提供了理论见解，可能为未来的网络架构提供信息。

排序理由 该集群包含一篇详细介绍深度神经网络理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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深度残差网络学习Wasserstein空间中的测地线

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该集群包含一篇详细介绍深度神经网络理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Kuo Gai, Shihua Zhang ·

    深度残差网络学习 Wasserstein 空间中的测地线

    arXiv:2102.09235v3 Announce Type: replace-cross Abstract: Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNNs, especially deep residual networks (ResNets), tend to learn during train…

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