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Found 3 papers, 1 papers with code
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis
In this study, we present and compare the features of four tools from literature to quantify the carbon footprint of DL.
An algorithm-based multiple detection influence measure for high dimensional regression using expectile
The identification of influential observations is an important part of data analysis that can prevent erroneous conclusions drawn from biased estimators.
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies
As a result, machine learning models do not generalize even when trained on imaging datasets that were captured to study the same variable of interest.
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