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A different smart checking procedure could help doctors keep away from mistakes—but it surely’s also alarming some surgeons and leading to sabotage.

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ICLR 2024(2024) (to seem) Preview Preview abstract Predictive uncertainty-a model's self recognition about its accuracy on an enter-is essential for both constructing sturdy styles by means of coaching interventions and for check-time applications for instance selective classification. We suggest a novel instance-conditioned reweighting strategy that captures predictive uncertainty applying an auxiliary network and unifies these teach- and check-time applications. The auxiliary community is experienced utilizing a meta-objective in a very bilevel optimization framework. A critical contribution of our proposal could be the meta-goal of reducing the dropout variance, an approximation of Bayesian Predictive uncertainty.

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