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English(EN) Approaching Backpropagation Accuracy with Weight Perturbation on MNIST

权重扰动在MNIST上接近反向传播的准确度

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新的研究论文探讨了权重扰动（WP）作为训练神经网络的替代反向传播（BP）方法的有效性。研究发现，在MNIST数据集上，WP可以达到接近BP的准确度水平，尤其是在大批量和充分训练的情况下。然而，与BP相比，WP显示出显著的效率差距，需要更多的训练周期才能达到可比的性能，尤其是在更深的网络配置中。研究表明，虽然WP在特定条件下可以近似BP的准确度，但其效率无法与BP相提并论。 AI

影响 探索了可能在神经网络开发中提供效率提升或新功能的替代训练方法。

排序理由 该集群包含一篇详细介绍神经网络新训练方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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权重扰动在MNIST上接近反向传播的准确度

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该集群包含一篇详细介绍神经网络新训练方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Patrick Inoue ·

    使用权重扰动在MNIST上达到反向传播精度

    arXiv:2610.21665v1 Announce Type: new Abstract: Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change in loss, without backpropagating derivatives. We examine how batch size, training duration, and estimator stabilization a…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Patrick Inoue ·

    使用权重扰动在MNIST上达到反向传播精度

    Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change in loss, without backpropagating derivatives. We examine how batch size, training duration, and estimator stabilization affect its attainable accuracy in fully connected…

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