The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study

We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width... (read more)

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