研究人员开发了一个新的理论框架来理解深度在深度神经网络中的作用。他们的工作量化了中间层如何近似目标函数，近似误差与精化的几何尺度相关。这种受多尺度深度学习启发的方法，通过在更精细的尺度上针对残差信息来实现渐进式精化，而无需重新设计先前的网络组件。 AI
影响 为理解网络深度提供了理论基础，可能指导未来的架构设计。
排序理由 关于深度神经网络理论方面的学术论文。
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新理论量化了深度在深度神经网络中的作用
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报道来源 [1]
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深度网络的分层几何近似率
Depth is widely viewed as a central contributor to the success of deep neural networks, whereas standard neural network approximation theory typically provides guarantees only for the final output and leaves the role of intermediate layers largely unclear. We address this gap by …