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English(EN) Shortcomings and capacities of real-constrained neural networks in complex spaces

使用HCIZ公式分析神经网络容量

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员分析了在复杂假设类别中强制执行实数预激活时神经网络的存储容量。他们的工作利用了Harish-Chandra-Itzykson-Zuber (HCIZ) 公式，这是一种在该领域不常用的方法，用于推导出渐近比的更稳定近似值。该方法通过在酉和正交紧致流形上进行积分，并采用Weyl积分公式和Haar测度，专门针对他们的研究进行了定制。 AI

排序理由 该集群包含一篇详细介绍神经网络特性理论分析的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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使用HCIZ公式分析神经网络容量

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该集群包含一篇详细介绍神经网络特性理论分析的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andrew Gracyk ·

    复杂空间中受限神经网络的局限性与能力

    arXiv:2606.04390v1 Announce Type: new Abstract: We find the asymptotic ratio between the storage capacities when enforcing real pre-activations in a complex hypothesis class as opposed to complex ones in the same class. Our methods depend on Gardner volume comparisons at critical…

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