Neural Networks as Linear Regression: An Introduction for Statisticians
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
6 of 18 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
- 2014Recurrent Neural Network Regularizationcited by 189
- 2019Neural network identifiability for a family of sigmoidal nonlinearitiescited by 14
Built on by
Citing papers per year: 2026: 1.
1 paper.