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Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain sampling, ensembling, or Monte Carlo dropout.
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Uncertainty Estimation Using a Single Deep Deterministic Neural Network
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Snapshot Ensembles: Train 1, Get M for Free
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