English(EN) Local large deviations for linear-region growth in random piecewise-linear networks

新论文为分段线性神经网络中的仿射区域增长建模

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-08 05:14

研究人员在arXiv上发表了一篇论文，详细介绍了一个用于深度分段线性神经网络中仿射区域增长的数学模型。该研究使用基于 tent map 扰动的随机组合模型来分析多层后的仿射分块数量。研究结果为增长率的尾部建立了指数界限，并引入了一个缺陷过程来推导下界，暗示了某些尾部行为的最终排除。 AI

影响为神经网络架构的结构和增长提供了理论见解。

排序理由该集群包含一篇在arXiv上发表的学术论文，详细介绍了数学研究。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Recep \"Ozkan, Christian Hirsch · 2026-07-09 04:00

随机分段线性网络中线性区域增长的局部大偏差

arXiv:2607.07014v1 Announce Type: cross Abstract: We study a random compositional model for the growth of affine regions in deep piecewise-linear networks. The model is generated by i.i.d.\ perturbations of the symmetric height-one tent map, and the main observable is the number …
arXiv stat.ML TIER_1 English(EN) · Christian Hirsch · 2026-07-08 05:14

随机分段线性网络中线性区域增长的局部大偏差

We study a random compositional model for the growth of affine regions in deep piecewise-linear networks. The model is generated by i.i.d.\ perturbations of the symmetric height-one tent map, and the main observable is the number \(N_n\) of affine pieces after \(n\) layers. We pr…