只需一层门控+跳跃连接，浅层网络可变深而保持分类能力

Deep one-gate per layer networks with skip connections are universal classifiers

• 用门控层与跳跃连接构造深层网络结构
• 两层网络经改造后仍能准确分类两类数据
• 适合研究网络深度与表达能力关系的学者

本文表明，一个设计用于分类两类数据点的两层多层感知机，可轻松转化为具有单门控层和跳跃连接的深层神经网络。该变换保持了原网络的分类性能，揭示了在特定结构下，深层网络可通过简单模块组合实现通用分类能力。

原文摘要 · Abstract (English)

This paper shows how a multilayer perceptron with two hidden layers, which has been designed to classify two classes of data points, can easily be transformed into a deep neural network with one-gate layers and skip connections.

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