# Neural Networks as Linear Regression: An Introduction for Statisticians

arXiv: https://arxiv.org/abs/2606.23601 · HTML: https://mlsanity.com/paper/2606.23601/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Neural network identifiability for a family of sigmoidal nonlinearities](https://mlsanity.com/paper/1906.06994/) (2019)

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

- [Dynamic Prediction of Alternating Recurrent Events via Neural Network](https://mlsanity.com/paper/2606.30889/) (2026)

## Abstract (arXiv)

Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remains high, particularly for classical statisticians trained in a frequentist perspective. In this letter, we demystify neural networks by describing networks that approximate a linear regression and describe common customizations that provide a foundation for further study.