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  2. Improving Deep Ensembles for Better Deep Uncertainty Quantification

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/improving_deep_ensembles.pdf
    15 Nov 2021: However, theextrapolations away from training samples, produced by models with these parameter sets candiffer greatly. ... The first dataset examined, chosen for popularity andfast experimentation, is MNIST (LeCun et al., 2010).
  3. Depth Uncertainty Networks for Active Learning

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/depth_uncertainty_networks_for_active_learning_reduced.pdf
    15 Nov 2021: Finally,we propose a modification to an information-based acquisition strategy, termed truncatedBALD, which mitigates bias caused by model misspecification. ... Bayesian deep learning provides a solution to the first two of these issues.

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