Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.
@article{arxiv.2211.16782,
title = {Panel Discussion: Practical Problem Solving for Machine Learning},
author = {Guillermo Cabrera and Sungwook E. Hong and Lilianne Nakazono and David Parkinson and Yuan-Sen Ting},
journal= {arXiv preprint arXiv:2211.16782},
year = {2025}
}
Comments
6 pages. Prepared for the proceedings of the International Astronomical Union Symposium 368 "Machine Learning in Astronomy: Possibilities and Pitfalls"