Convergence of gradient descent for learning linear neural networks

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Gradient descent demonstrates convergence to a global minimum for deep linear neural networks.
For almost all initializations, the method leads to a global minimum with two layers, optimizing the loss function.
Analysis involves assessing convergence properties and conditions on the stepsizes in training neural networks with multiple layers long-term outcomes with gradient descent's limitations are discussed.

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Demonstrates gradient descent converges to a global minimum in deep linear neural networks, suggesting improved training methods.

Nguegnang et al. (2024) studied this question.

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