Universality of Gradient Descent in Neural Network Training

The paper proves that any successful neural network training algorithm can be emulated by gradient descent through network extension, establishing a theoretical universality result. This implies gradient descent can replicate optimal weights for classification tasks when given appropriate network modifications.

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