Gradients as a Measure of Uncertainty in Neural Networks

18 Aug 2020Jinsol LeeGhassan AlRegib

Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters inputs with unfamiliar conditions. Detecting such inputs is vital to preventing models from making naive predictions that may jeopardize real-world applications of neural networks... (read more)

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