Researchers analyze critical organization in deep neural networks

Researchers have rigorously studied the thermodynamic limit of deep neural networks (DNNs) and recurrent neural networks (RNNs), focusing on sigmoid activation functions. They demonstrated that in a specific parameter region, these networks exhibit a unique state that transitions to infinitely many states outside this region, a phenomenon termed critical organization. The study also utilizes p-adic integers to represent hierarchical structures within these networks, connecting critical organization to p-adic tree-like structures and analyzing a toy model of a hierarchical edge detector. AI

IMPACT Provides theoretical insights into the behavior and structure of deep learning models.

RANK_REASON The cluster contains an academic paper detailing theoretical research on neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Deep Neural Networks
• p-adic integers
• Recurrent Neural Networks
• W. A. Zuniga-Galindo

• paper
• other

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