# Backprop Evolution

arXiv: https://arxiv.org/abs/1808.02822 · HTML: https://mlsanity.com/paper/1808.02822/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Re-parameterizing Your Optimizers rather than Architectures](https://mlsanity.com/paper/2205.15242/) (2022)
- [Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization](https://mlsanity.com/paper/2304.03374/) (2023)

## Abstract (arXiv)

The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to discover new variations of the back-propagation equation. We use a domain specific lan- guage to describe update equations as a list of primitive functions. An evolution-based method is used to discover new propagation rules that maximize the generalization per- formance after a few epochs of training. We find several update equations that can train faster with short training times than standard back-propagation, and perform similar as standard back-propagation at convergence.