# On Polynomial Approximation of Activation Function

arXiv: https://arxiv.org/abs/2202.00004 · HTML: https://mlsanity.com/paper/2202.00004/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression](https://mlsanity.com/paper/2304.03807/) (2023)
- [Privacy-Preserving 3-Layer Neural Network Training](https://mlsanity.com/paper/2308.09531/) (2023)
- [LFFR: Logistic Function For (single-output) Regression](https://mlsanity.com/paper/2407.09955/) (2024)

## Abstract (arXiv)

In this work, we propose an interesting method that aims to approximate an activation function over some domain by polynomials of the presupposing low degree. The main idea behind this method can be seen as an extension of the ordinary least square method and includes the gradient of activation function into the cost function to minimize.