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Found 3 papers, 2 papers with code
Quantum Machine Learning for Radio Astronomy
In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network.
Weight Pruning and Uncertainty in Radio Galaxy Classification
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 posterior variance for individual test samples is correlated with human uncertainty when labelling radio galaxies.
E(2) Equivariant Self-Attention for Radio Astronomy
In this work we introduce group-equivariant self-attention models to address the problem of explainable radio galaxy classification in astronomy.
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