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Empirical Strategy for Stretching Probability Distribution in Neural-network-based Regression
In this study, we considered the distribution error, i. e., the inconsistency of two distributions (those of the predicted values and label), as the prediction error, and proposed weighted empirical stretching (WES) as a novel loss function to increase the overlap area of the two distributions.
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