<ul><li>Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks</li><li>Deep learning theory seeks to understand how neural networks learn hierarchical features.</li><li>This study focuses on three-layer neural networks and their richer feature learning capabilities.</li><li>They present a theorem that bounds sample complexity and width needed for low test error when the target has hierarchical structure.</li></ul>

Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks

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