Gradient Descent Convergence Proven for Wide Shallow Neural Networks

Researchers have theoretically analyzed the convergence properties of gradient descent in training wide, shallow neural networks with bounded nonlinearities. Their work extends previous findings beyond simple ReLU or sigmoid activations to more complex architectures like multi-head attention layers and two-layer sigmoid networks with vector output weights. The study proves that non-global minimizers are unstable under gradient descent dynamics, ensuring convergence to global minimizers when initial parameters have full support. AI

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IMPACT Provides theoretical guarantees for training complex neural network architectures, potentially informing future model design and optimization techniques.

RANK_REASON Academic paper detailing theoretical analysis of model training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

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• gradient descent
• neural networks
• Chizat and Bach

• paper
• other