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Examining Redundancy in the Context of Safe Machine Learning (2007.01900v1)
Published 3 Jul 2020 in cs.LG and stat.ML
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.