Partial differential equation regularization for supervised machine learning

Understand

This article is an overview of supervised machine learning problems for regression and classification.

• Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds.
• Implicit regularization in deep learning examples are presented, including data augmentation, adversarial training, and additive noise.
• These methods are reframed as explicit gradient regularization.