arXiv:2606.15669v1 Announce Type: cross Abstract: Modern deep neural networks rely on Euclidean scalar activations (e.g., ReLU) and global normalization techniques (e.g., LayerNorm) to prevent gradient instability in deep architectures. However, these mechanisms inherently cause dead neurons, discard critical directional information, and destroy the orthogonality of feature representations. Inspired by the frequency-modulation transmission of biological axons, we propose the Z-Plane Neural Network, which maps hidden states into 2D phasor bundles on a hypersphere. We introduce a novel geometric
Ongoing research into improving the fundamental mechanisms of neural networks drives innovation in activation functions and normalization to overcome current limitations.
This development proposes a novel architectural component that could significantly enhance the stability and information retention of deep neural networks, impacting future AI capabilities.
Traditional Euclidean scalar activations and global normalization methods might be replaced by a geometric approach, potentially leading to more robust and efficient AI models.
- · AI researchers
- · Deep learning practitioners
- · AI development platforms
- · Developers reliant on current ReLU and LayerNorm limitations
- · AI models that struggle with gradient instability
Increased efficiency and performance of deep learning models, particularly in complex architectures.
Faster development and deployment of more sophisticated AI applications across various industries.
Accelerated progress in areas requiring highly stable and robust deep learning, potentially impacting fields from autonomous systems to scientific discovery.
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Read at arXiv cs.AI