# Simplifying Neural Networks using Formal Verification

arXiv: https://arxiv.org/abs/1910.12396 · HTML: https://mlsanity.com/paper/1910.12396/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Shielding Atari Games with Bounded Prescience](https://mlsanity.com/paper/2101.08153/) (2021)

## Abstract (arXiv)

Deep neural network (DNN) verification is an emerging field, with diverse verification engines quickly becoming available. Demonstrating the effectiveness of these engines on real-world DNNs is an important step towards their wider adoption. We present a tool that can leverage existing verification engines in performing a novel application: neural network simplification, through the reduction of the size of a DNN without harming its accuracy. We report on the work-flow of the simplification process, and demonstrate its potential significance and applicability on a family of real-world DNNs for aircraft collision avoidance, whose sizes we were able to reduce by as much as 10%.