Examining Redundancy in the Context of Safe Machine Learning

• URL: http://arxiv.org/abs/2007.01900v1
• Date: Fri, 3 Jul 2020 18:23:56 GMT
• Title: Examining Redundancy in the Context of Safe Machine Learning
• Authors: Hans Dermot Doran and Monika Reif
• Abstract summary: This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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