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Minimum norm solutions do not always generalize well for over-parameterized problems.

arXiv: Machine Learning, (2018)

Cited by: 3|Views27
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Abstract

Stochastic gradient descent is the de facto algorithm for training deep neural networks (DNNs). Despite its popularity, it still requires fine hyper-parameter tuning in order to achieve its best performance. This has led to the development of adaptive methods, that claim automatic hyper-parameter tuning. Recently, researchers have studied...More

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