Sumio Watanabe
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2026
A Statistical Difference between Single-Layer Learning and Hierarchical Learning in Wide Neural Networks
This paper compares two theoretical frameworks for hierarchical neural networks with a finite but large number of hidden units and shows that training input-to-hidden weights reduces generalization error and avoids singularities.
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