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Updated: Jul 1, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
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使用深度ReLU神经网络在光滑多元组件上对多变量函数的低维近似和概括
1Department of Applied Mathematics, University of Houston, 651 Phillip G Hoffman, Houston, 77204-3008, TX, USA.
概括
深度神经网络有效地近似复杂的功能,克服了维度的诅咒. 它们的性能取决于函数的内在多重维度 (d),而不是环境空间维度 (D).
科学领域:
- 计算数学是指计算数学.
- 机器学习理论机器学习理论
- 数字分析 数字分析
背景情况:
- 深度神经网络 (DNN) 具有显著的函数近似能力,在高维空间中表现优于传统方法.
- "维度的诅咒"传统上限制了数值解决器在高维设置中的有效性.
- 了解DNN成功的理论基础,特别是与维度相关的理论基础,至关重要.
研究的目的:
- 提供一个理论框架,解释为什么DNN在高维函数近似中成功.
- 在嵌入在高维空间中的低维多元组上分析DNN对霍尔德函数的近似和概括错误.
- 为DNN建立维度独立的趋同估计.
主要方法:
- 对近似和概括错误的统一收估计的分析.
- 对具有 ReLU 激活功能的深度神经网络进行理论研究.
- 专注于定义在一个嵌入RD的d维光滑多元体M上的霍尔德函数,其中d
主要成果:
- 证明DNN的近似和概括错误是独立于环境维度 (D).
- 证明这些错误仅取决于函数的内在多重维度 (d).
- 建立了精确的统一收估计,改进了现有文献.
结论:
- 在处理高维函数时,DNN具有理论上的优势,因为它们能够利用低维结构.
- 数据的内在维度 (多重维度) 是关键因素,而不是环境空间维度.
- 这一发现为DNN在复杂,高维度机器学习任务中的实际成功提供了理论解释.
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