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Ensembling neural networks is an effective way to increase accuracy, and can often match the performance of individual larger models.
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When ensembling smaller models is more efficient than single large models
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
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CINIC-10 is not ImageNet or CIFAR-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
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BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning
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The evolution of out-of-distribution robustness throughout fine-tuning
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Training independent subnetworks for robust prediction
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Deep ensembles from a bayesian perspective
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
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Calibrated ensembles: A simple way to mitigate ID-OOD accuracy tradeoffs
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
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Uncertainty quantification and deep ensembles
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Neural ensemble search for uncertainty estimation and dataset shift
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Diversity and generalization in neural network ensembles
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Surprises in high-dimensional ridgeless least squares interpolation
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