DAEMA: Denoising Autoencoder with Mask Attention

Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing data imputation has become an active research area, in which recent deep learning approaches have achieved state-of-the-art results. We propose DAEMA: Denoising Autoencoder with Mask Attention.

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Estimating Expected Calibration Errors

Uncertainty in probabilistic classifiers predictions is a key concern when models are used to support human decision making, in broader probabilistic pipelines or when sensitive automatic decisions have to be taken.
Studies have shown that most models are not intrinsically well calibrated, meaning that their decision scores are not consistent.

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Towards a Continuous Evaluation of Calibration

For safety-critical systems involving AI components (such as in planes, cars, or healthcare), safety and associated certification tasks are one of the main challenges, which can become costly and difficult to address.

One key aspect is to ensure that the decisions a machine-learning classifier makes are properly calibrated.

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