English(EN) Beyond Backpropagation: Monte Carlo Method Can Train Deep Neural Networks

蒙特卡洛法为训练深度神经网络提供无梯度替代方案

作者 PulseAugur 编辑部 · [3 个来源] · 2026-07-09 12:31

研究人员展示了一种使用简单蒙特卡洛算法训练深度神经网络的无梯度方法。该方法通过随机变异参数并在损失降低时保留它们，从而绕过了反向传播的常见问题，例如梯度消失和爆炸。该方法已被证明在训练具有20多层的网络甚至Transformer架构以完成图像分类和语言建模等任务方面是有效的。 AI

影响这种无梯度方法可能为训练复杂的AI模型提供新的途径，有可能简化训练过程并克服当前基于梯度方法的局限性。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-09 12:31

超越反向传播：蒙特卡洛方法可训练深度神经网络

Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles -- vanishing and exploding gradients. The pursuit of gradient-free methods has long been a goal in the field of artificial intelligence. This paper shows that indeed the …
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超越反向传播：蒙特卡洛方法可训练深度神经网络

arXiv:2607.08406v1 Announce Type: cross Abstract: Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles -- vanishing and exploding gradients. The pursuit of gradient-free methods has long been a goal in the field of artifici…
arXiv stat.ML TIER_1 English(EN) · Hong Zhao · 2026-07-09 12:31

超越反向传播：蒙特卡洛方法可训练深度神经网络

Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles -- vanishing and exploding gradients. The pursuit of gradient-free methods has long been a goal in the field of artificial intelligence. This paper shows that indeed the …