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Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge.
On a measure of the information provided by an experiment
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Multi-class active learning for image classification
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Bayesian learning for neural networks , volume 118
R. M. Neal · 2012
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Gaussian processes for big data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
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Adaptive active learning for image classification
X. Li and Y. Guo · 2013
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Rectifier nonlinearities improve neural network acoustic models
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Learning word embeddings efficiently with noise-contrastive estimation
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D. M. Blei · 2014
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Mapping gaussian process priors to bayesian neural networks
D. Flam-Shepherd, J. Requeima, and D. Duvenaud · 2017
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Model selection in bayesian neural networks via horseshoe priors
S. Ghosh and F. Doshi-Velez · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Automatic differentiation variational inference
A. Kucukelbir, D. Tran, R. Ranganath, A. Gelman, and D. M. Blei · 2017
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Bayesian compression for deep learning
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Regularizing neural networks by penalizing confident output distributions
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Cost-effective active learning for deep image classification
K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin · 2017
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Uncertainty decomposition in bayesian neural networks with latent variables
S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft · 2018
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Principled detection of out-of-distribution examples in neural networks
S. Liang, Y. Li, and R. Srikant · 2018
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Predictive uncertainty estimation via prior networks
A. Malinin and M. Gales · 2018
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Bayesian layers: A module for neural network uncertainty
D. Tran, D. Mike, M. van der Wilk, and D. Hafner · 2018
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mixup: Beyond empirical risk minimization
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Functional variational bayesian neural networks
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