Deep one-gate per layer networks with skip connections are universal classifiers
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909893927108608 |
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| author | Rojas, Raul |
| author_facet | Rojas, Raul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep one-gate per layer networks with skip connections are universal classifiers Rojas, Raul Machine Learning Artificial Intelligence I.2.0 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. |
| title | Deep one-gate per layer networks with skip connections are universal classifiers |
| topic | Machine Learning Artificial Intelligence I.2.0 |
| url | https://arxiv.org/abs/2511.05552 |