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

This addresses the problem of network architecture design for universal classification in machine learning, but appears incremental as it builds on existing multilayer perceptron structures.

The paper demonstrates that a two-hidden-layer multilayer perceptron for binary classification can be transformed into a deep neural network with one-gate layers and skip connections, achieving universal classification capabilities.