Classical theory that guides the design of nonparametric prediction methods like deep neural networks involves a tradeoff between the …

Much recent theoretical work has concentrated on “solving deep learning”. Yet, deep learning is not a thing in itself and …

Inductive biases from specific training algorithms like stochastic gradient descent play a crucial role in learning overparameterized …

Machine learning has made tremendous progress over the last decade. It’s thus tempting to believe that ML techniques are a …

Algorithms in deep learning have a regularization effect: different optimizers with different hyper-parameters, on the same training …

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