Minimum weight norm models do not always generalize well for over-parameterized problems

Understand

This work is substituted by the paper in arXiv:2011.14066.

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