Generalization of Gradient Descent in Over-Parameterized ReLU Networks: Insights from Minima Stability and Large Learning Rates

Gradient descent-trained neural networks operate effectively even in overparameterized settings with random weight initialization, often finding global optimum solutions despite the non-convex nature of the problem. These solutions, achieving zero training error, surprisingly do not overfit in many cases, a phenomenon known as “benign overfitting.” However, for ReLU networks, interpolating solutions can lead to overfitting. […]

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