# Examining Redundancy in the Context of Safe Machine Learning

arXiv: https://arxiv.org/abs/2007.01900 · HTML: https://mlsanity.com/paper/2007.01900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

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

- [Dependable Neural Networks Through Redundancy, A Comparison of Redundant Architectures](https://mlsanity.com/paper/2108.02565/) (2021)

## Abstract (arXiv)

This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. The purpose is to investigate na"ive implementations of redundant architectures as a first step towards safe and dependable machine learning. We report on a set of measurements using the MNIST database which ultimately serve to underline the expected difficulties in using NN classifiers in safe and dependable systems.