| Radio Galaxy Zoo: using semi-supervised learning to leverage large unlabelled data sets for radio galaxy classification under data set shift IV Slijepcevic, AMM Scaife, M Walmsley, M Bowles, OI Wong, SS Shabala, ... Monthly Notices of the Royal Astronomical Society 514 (2), 2599-2613, 2022 | 28 | 2022 |
| Towards Galaxy Foundation Models with Hybrid Contrastive Learning M Walmsley, IV Slijepcevic, M Bowles, AMM Scaife ICML 2022: Machine Learning for Astrophysics Workshop, 2022 | 15 | 2022 |
| Radio Galaxy Zoo: Towards building the first multi-purpose foundation model for radio astronomy with self-supervised learning IV Slijepcevic, AMM Scaife, M Walmsley, M Bowles, O Wong, SS Shabala, ... RAS Techniques and Instruments (RASTI), 2023 | 8 | 2023 |
| Zoobot: Adaptable Deep Learning Models for Galaxy Morphology M Walmsley, C Allen, B Aussel, M Bowles, K Gregorowicz, IV Slijepcevic, ... Journal of Open Source Software 8 (85), 5312, 2023 | 8 | 2023 |
| Learning useful representations for radio astronomy" in the wild" with contrastive learning IV Slijepcevic, AMM Scaife, M Walmsley, M Bowles ICML 2022: Machine Learning for Astrophysics Workshop, 2022 | 7 | 2022 |
| Radio galaxy zoo EMU: towards a semantic radio galaxy morphology taxonomy M Bowles, H Tang, E Vardoulaki, EL Alexander, Y Luo, L Rudnick, ... Monthly Notices of the Royal Astronomical Society 522 (2), 2584-2600, 2023 | 5 | 2023 |
| Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification? IV Slijepcevic, AMM Scaife NeurIPS 2021: Fourth Workshop on Machine Learning and the Physical Sciences, 2021 | 4 | 2021 |
| A New Task: Deriving Semantic Class Targets for the Physical Sciences M Bowles, H Tang, E Vardoulaki, EL Alexander, Y Luo, L Rudnick, ... NeurIPS 2022: Machine Learning and the Physical Sciences Workshop, 2022 | 1 | 2022 |
