Batch gradient descent achieves linear convergence to zero MSE with high probability for sufficiently wide shallow NNs with non-affine piecewise affine activations and distinct inputs.
How SGD Selects the Global Minima in Over-parameterized Lea rning: A Dynamical Stability Perspective
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Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation
Batch gradient descent achieves linear convergence to zero MSE with high probability for sufficiently wide shallow NNs with non-affine piecewise affine activations and distinct inputs.