Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel

ICLR 2020 Xin QiuElliot MeyersonRisto Miikkulainen

Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point prediction is not enough: the uncertainty (i.e. risk or confidence) of that prediction must also be estimated... (read more)

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