PReLU: Yet Another Single-Layer Solution to the XOR Problem
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916397204897792 |
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| author | Pinto, Rafael C. Tavares, Anderson R. |
| author_facet | Pinto, Rafael C. Tavares, Anderson R. |
| contents | This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show that the single-layer PReLU network can achieve 100\% success rate in a wider range of learning rates while using only three learnable parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PReLU: Yet Another Single-Layer Solution to the XOR Problem Pinto, Rafael C. Tavares, Anderson R. Neural and Evolutionary Computing Artificial Intelligence Machine Learning This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show that the single-layer PReLU network can achieve 100\% success rate in a wider range of learning rates while using only three learnable parameters. |
| title | PReLU: Yet Another Single-Layer Solution to the XOR Problem |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.10821 |