A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues. (arXiv:2107.08574v2 [cs.LG] UPDATED)

Data missingness and quality are common problems in machine learning,
especially for high-stakes applications such as healthcare. Developers often
train machine learning models on carefully curated datasets using only high
quality data; however, this reduces the utility of such models in production
environments. We propose a novel neural network modification to mitigate the
impacts of low quality and missing data which involves replacing the fixed
weights of a fully-connected layer with a function of an additional input. This
is inspired …

arxiv data data quality data quality issues network neural network quality robustness