**arXiv ID:** 2409.10821 **Authors:** Rafael C. Pinto, Anderson R. Tavares **Published:** 2024-09-17T01:28:40Z **Abstract:** 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 th...
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# PReLU: Yet Another Single-Layer Solution to the XOR Problem
**arXiv ID:** 2409.10821
**Authors:** Rafael C. Pinto, Anderson R. Tavares
**Published:** 2024-09-17T01:28:40Z
**Abstract:**
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.
## Skill Description
This skill is generated from the arXiv paper: PReLU: Yet Another Single-Layer Solution to the XOR Problem (2409.10821).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2409.10821](http://arxiv.org/abs/2409.10821v1)
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