通过多重拓学理论研究神经网络表达力
Jiachen Yao1, Lingjie Yi1, Mayank Goswami2
1Stony Brook University.
概括
这项研究通过分析数据的多样性来探索神经网络的大小. 我们整合了拓学和几何学,为ReLU网络建立了一个尺寸上限,改善了对网络表达力的理解.
科学领域:
- 机器学习 机器学习
- 计算拓学的计算拓学
- 数据科学数据科学数据科学
背景情况:
- 现实世界的数据通常存在于低维的多元体上.
- 神经网络的大小受这些数据的复杂性影响.
- 几何和拓性质对于理解多元体至关重要.
研究的目的:
- 为了研究神经网络表达力,涉及到隐性数据的多样性.
- 整合数据多元体的拓和几何特征.
- 为了获得ReLU神经网络大小的上限.
主要方法:
- 数据多元体几何学的分析.
- 纳入拓数据分析.
- 开发一个理论框架,用于估计网络大小.
主要成果:
- 建立了一个新的ReLU神经网络大小的上限.
- 这项研究证明了多元拓在确定网络大小方面的重要性.
- 提供了一种方法来估计基于多重特征的网络大小.
结论:
- 网络大小从根本上与数据多元体的拓和几何复杂性有关.
- 整合拓和几何学可以更全面地了解神经网络容量.
- 这些发现有助于有效设计用于真实世界的数据的神经网络.
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