Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference

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Earlier work this paper cites.

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Earlier work this paper cites.

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Earlier work this paper cites.

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Earlier work this paper cites.

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Earlier work this paper cites.

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Original

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