Neural Network with Unbounded Activation Functions is Universal Approximator

14 May 2015 Sho Sonoda Noboru Murata

This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning. The ReLU network can be analyzed by the ridgelet transform with respect to Lizorkin distributions... (read more)

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METHOD TYPE
ReLU
Activation Functions