# Activation function impact on Sparse Neural Networks

arXiv: https://arxiv.org/abs/2010.05943 · HTML: https://mlsanity.com/paper/2010.05943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

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

- [Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization](https://mlsanity.com/paper/2102.01670/) (2021)
- [Truly Sparse Neural Networks at Scale](https://mlsanity.com/paper/2102.01732/) (2021)
- [Learning Activation Functions for Sparse Neural Networks](https://mlsanity.com/paper/2305.10964/) (2023)

## Abstract (arXiv)

While the concept of a Sparse Neural Network has been researched for some time, researchers have only recently made notable progress in the matter. Techniques like Sparse Evolutionary Training allow for significantly lower computational complexity when compared to fully connected models by reducing redundant connections. That typically takes place in an iterative process of weight creation and removal during network training. Although there have been numerous approaches to optimize the redistribution of the removed weights, there seems to be little or no study on the effect of activation functions on the performance of the Sparse Networks. This research provides insights into the relationship between the activation function used and the network performance at various sparsity levels.