# The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data

@article{Rodrigues2021TheIO, title={The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data}, author={Nat'alia V. N. Rodrigues and L. Raul Abramo and Nina Sumiko Tomita Hirata}, journal={ArXiv}, year={2021}, volume={abs/2108.04742} }

Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise. This model mimics…

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