Dr Micah Bowles: Publications

Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift

ArXiv 2204.08816 (2022)

Authors:

Inigo V Slijepcevic, Anna MM Scaife, Mike Walmsley, Micah Bowles, Ivy Wong, Stanislav S Shabala, Hongming Tang

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Quantifying uncertainty in deep learning approaches to radio galaxy classification

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 511:3 (2022) 3722-3740

Authors:

Devina Mohan, Anna MM Scaife, Fiona Porter, Mike Walmsley, Micah Bowles

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Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification

ArXiv 2201.01203 (2022)

Authors:

Devina Mohan, Anna MM Scaife, Fiona Porter, Mike Walmsley, Micah Bowles

Details from ArXiV

E(2) Equivariant Self-Attention for Radio Astronomy

ArXiv 2111.04742 (2021)

Authors:

Micah Bowles, Matthew Bromley, Max Allen, Anna Scaife

Details from ArXiV

Structured variational inference for simulating populations of radio galaxies

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 503:3 (2021) 3351-3370

Authors:

David J Bastien, Anna MM Scaife, Hongming Tang, Micah Bowles, Fiona Porter

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Dr Micah Bowles

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Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift

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Quantifying uncertainty in deep learning approaches to radio galaxy classification

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Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification

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E(2) Equivariant Self-Attention for Radio Astronomy

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Structured variational inference for simulating populations of radio galaxies

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Dr Micah Bowles

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Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift

Authors:

Quantifying uncertainty in deep learning approaches to radio galaxy classification

Authors:

Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification

Authors:

E(2) Equivariant Self-Attention for Radio Astronomy

Authors:

Structured variational inference for simulating populations of radio galaxies

Authors: