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

Quantifying uncertainty in deep learning approaches to radio galaxy classification

Monthly Notices of the Royal Astronomical Society 91̽�� University Press (OUP) 511:3 (2022) 3722-3740

Authors:

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

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

E(2) Equivariant Self-Attention for Radio Astronomy

ArXiv 2111.04742 (2021)

Authors:

Micah Bowles, Matthew Bromley, Max Allen, Anna Scaife

Structured variational inference for simulating populations of radio galaxies

Monthly Notices of the Royal Astronomical Society 91̽�� 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

Authors:

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: