On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective.

arXiv:2603.02095v2 Announce Type: replace-cross Abstract: We study the convergence dynamics of Gradient Descent (GD) in a minimal binary classification setting, consisting of a two-neuron ReLU network and two training instances. We prove that even under these