ResNet with one-neuron hidden layers is a Universal Approximator
We demonstrate that a very deep ResNet with stacked modules that have one neuron per hidden layer and ReLU activation functions can uniformly approximate any Lebesgue integrable function in d dimensions, i.e. ℓ1(Rd). Due to the identity mapping inherent to ResNets, our network has alternating layers...
| Main Authors: | , |
|---|---|
| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Morgan Kaufmann Publishers,
2021-01-07T14:35:57Z.
|
| Subjects: | |
| Online Access: | Get fulltext |