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The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions.
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What uncertainties do we need in bayesian deep learning for computer vision?
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Predictive uncertainty estimation via prior networks
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
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Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
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Masksembles for uncertainty estimation
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Training independent subnetworks for robust prediction
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Deep6ma: A deep learning framework for exploring similar patterns in dna n6-methyladenine sites across different species
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Training of multiple and mixed tasks with a single network using feature modulation
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