Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification

  title={Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification},
  author={Devina Mohan and Anna M. M. Scaife and Fiona Porter and Mike Walmsley and Micah Bowles},
In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification. We show that the level of model posterior variance for individual test samples is correlated with human uncertainty when labelling radio galaxies. We explore the model performance and uncertainty calibration for a variety of different weight priors and suggest that a sparse prior produces more well-calibrated uncertainty estimates. Using the… 

Using Bayesian Deep Learning to Infer Planet Mass from Gaps in Protoplanetary Disks

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Probabilistic learning for pulsar classification

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Annals of Mathematical Statistics

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Experimental Astronomy

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