PReLU: Yet Another Single-Layer Solution to the XOR Problem
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 17 Sep 2024.
Builds on
6 of 12 references.
References · 6
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2024Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networkscited by 21
- 2021Layer Folding: Neural Network Depth Reduction using Activation Linearizationcited by 14
- 2021Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networkscited by 11
- 2021A brief history of AI: how to prevent another winter (a critical review)cited by 8
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.