mlsanityData to 14 Oct 2026

Neural Networks as Linear Regression: An Introduction for Statisticians

arXiv 2606.23601 ↗First posted 22 Jun 2026stat.ML cs.LGcited by 1

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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.

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6 of 18 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2014Recurrent Neural Network Regularizationcited by 189
  6. 2019Neural network identifiability for a family of sigmoidal nonlinearitiescited by 14

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