# Partial differential equation regularization for supervised machine learning

arXiv: https://arxiv.org/abs/1910.01612 · HTML: https://mlsanity.com/paper/1910.01612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Generalization Error Analysis of Neural networks with Gradient Based Regularization](https://mlsanity.com/paper/2107.02797/) (2021)

## Abstract (arXiv)

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.