17 citations · 20 across the 9 of their papers we have counts for
4 papers · 1 filter
Monte Carlo conformal prediction for quantifying uncertainty in radio galaxy classification under ambiguous ground truth
Alex Walls, James Barry, Devina Mohan +1
Dramatically increasing data volumes are forcing astronomers to adopt automated methods for the identification and classification of astronomical objects. Although deep-learning mo…
Natural gradient descent for improving variational inference based classification of radio galaxies
Devina Mohan, Anna M. M. Scaife
Bayesian neural networks (BNNs) are most commonly optimised with first-order optimisers such as stochastic gradient descent. However, when optimising for parameters of probabilisti…
MCMC to address model misspecification in Deep Learning classification of Radio Galaxies
Devina Mohan, Anna Scaife
The radio astronomy community is adopting deep learning techniques to deal with the huge data volumes expected from the next-generation of radio observatories. Bayesian neural netw…
Weight Pruning and Uncertainty in Radio Galaxy Classification
Devina Mohan, Anna Scaife
In this work we use variational inference to quantify the degree of epistemic uncertainty in model predictions of radio galaxy classification and show that the level of model poste…