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On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective
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 strong simplifying assumptions, while GD successfully converges to an optimal robustness margin, e
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