arXiv Machine Learning August 13, 2026 By Siqiao Mu, Diego Klabjan

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness

Read the original on arXiv Machine Learning →

arXiv:2608. 11479v1 Announce Type: new Abstract: We establish convergence guarantees of gradient descent for general feedforward neural networks of arbitrary width or depth, with no special requirements on the initialization or dataset.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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