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There exists a rich literature of astronomical uses of AI/ML, here are some interesting papers/talks that might be relevant. See also this set of PASP papers on machine learning in astronomy

Image and image artifact classification

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Brian Kent's demo for the 2023 June AAS splinter session, classifying VLASS sources with a neural network

Point source detection: DEEPSOURCE: point source detection using deep learning

Interferometric imaging with machine learning

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First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006 (Dabbech et al. 2022; ADS); R2D2 algorithm https://arxiv.org/pdf/2403.05452

3D detection and characterization of ALMA sources through deep learning ADS Github

ESO's BRAIN study (ALMA dev. program) ArXiv

RFI detection and excision

DSC based Dual-Resunet for radio frequency interference identification
U-net based "A Self-learning Neural Network Approach for Radio Frequency Interference Detection and Removal in Radio Astronomy". Here is the Github repo

Other topics

https://www.newyorker.com/magazine/2023/11/20/a-coder-considers-the-waning-days-of-the-craft

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PyTorch (open source code originally from Facebook)

Kaggle (community published models and code)

Meetings/conferences

ESOGPT (September 2024)

Rare gems in big data, Tucson, May 20-23 2024

International Conference on Machine Learning for Astrophysics ML4ASTRO2

NVidia Deep Learning Institute courses