PReLU: Yet Another Single-Layer Solution to the XOR Problem
<|reference_start|>PReLU: Yet Another Single-Layer Solution to the XOR Problem: 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.<|reference_end|>
@article{pinto2024prelu:,
    title={PReLU: Yet Another Single-Layer Solution to the XOR Problem},
    author={Rafael C. Pinto, Anderson R. Tavares},
    journal={arXiv preprint arXiv:2409.10821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10821},
    primaryClass={cs.NE cs.AI cs.LG}
}