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Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial.
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End to end learning for self-driving cars
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Tim Pearce, Mohamed Zaki, Alexandra Brintrup, N Anastassacos, and A Neely · 2018
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