Z-Plane Neural Networks Replace ReLU and LayerNorm for Stable Deep Learning

Researchers have introduced a novel neural network architecture called the Z-Plane Neural Network, which replaces traditional activation functions like ReLU and normalization techniques like LayerNorm. This new approach maps hidden states into 2D phasor bundles on a hypersphere, utilizing a geometric activation function called Radial Bounding. This method aims to prevent gradient instability, avoid dead neurons, and preserve directional information. A 100-layer Z-Plane Multi-Layer Perceptron demonstrated successful convergence and numerical stability on the MNIST dataset, achieving 98.34% accuracy without ReLU or LayerNorm, suggesting that bounded geometric activation alone is sufficient for deep learning. AI

IMPACT Introduces a novel activation and normalization method that could improve stability and information preservation in deep learning models.

RANK_REASON The cluster contains an academic paper detailing a new neural network architecture and its performance on a benchmark dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• LayerNorm
• MNIST database
• Radial Bounding
• ReLU
• Z-Plane Multi-Layer Perceptron
• Z-Plane Neural Network

• paper
• model release

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