Scaife, Anna M. M.

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

Walmsley, M., Scaife, A. M. M., Lintott, C., Lochner, M., Etsebeth, V., Géron, T., Dickinson, H., Fortson, L., Kruk, S., Masters, K. L., et al. (2021) · arXiv

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 64

Zoobot: Adaptable Deep Learning Models for Galaxy Morphology

Walmsley, M., Allen, C., Aussel, B., Bowles, M., Gregorowicz, K., Slijepcevic, I. V., Lintott, C. J., Scaife, A. M. M., Jabłońska, M., Karchev, K., et al. (2023) · Journal of Open Source Software

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 45

Fanaroff–Riley classification of radio galaxies using group-equivariant convolutional neural networks

Scaife, A. M. M., & Porter, F. (2021) · Monthly Notices of the Royal Astronomical Society

Advanced Neural Network ApplicationsGalaxies: Formation, Evolution, Phenomena

Cited by 44

Quantifying uncertainty in deep learning approaches to radio galaxy classification

Mohan, D., Scaife, A. M. M., Porter, F., Walmsley, M., & Bowles, M. (2022) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaRadio Astronomy Observations and Technology

Cited by 16

E(2) Equivariant Self-Attention for Radio Astronomy

Bowles, M., Bromley, M., Allen, M., & Scaife, A. (2021) · arXiv

Radio Astronomy Observations and TechnologyGalaxies: Formation, Evolution, Phenomena

Cited by 3

Scaling Laws for Galaxy Images

Walmsley, M., Bowles, M., Scaife, A. M. M., Makechemu, J. S., Gordon, A. J., Ferguson, A. M. N., Mann, R. G., Pearson, J., Popp, J. J., Bovy, J., et al. (2024) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 2