一篇新研究论文分析了深度神经网络中爆炸和消失梯度的现象，重点关注残差连接的影响。该研究利用乘法遍历理论以及Furstenberg和Kifer对Lyapunov指数的刻画，对Lyapunov谱及其受残差连接影响的方式给出了精确的表述。 AI
影响 为深度神经网络训练动力学提供了理论见解，可能为未来的模型架构提供信息。
排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了对深度神经网络行为的理论分析。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.17013v1 Announce Type: cross Abstract: The well known phenomenon of exploding and vanishing gradients in deep neural networks is analyzed using multiplicative ergodic theory. The effect of adding a residual connection is explained in this context. Specifically, a chara…
The well known phenomenon of exploding and vanishing gradients in deep neural networks is analyzed using multiplicative ergodic theory. The effect of adding a residual connection is explained in this context. Specifically, a characterization of Liapunov exponents due to Furstenbe…