"Constructing Impactful Machine Learning Research for Astronomy: Best ..."

Daniela Huppenkothen et al. (2023)

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DOI: 10.48550/ARXIV.2310.12528

access: open

type: Informal or Other Publication

metadata version: 2024-10-06

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  - journals/corr/abs-2310-12528
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  - https://dblp.org/rec/journals/corr/abs-2310-12528
Daniela Huppenkothen, Michelle Ntampaka, M. Ho, M. Fouesneau, Brian Nord, Joshua E. G. Peek, Mike Walmsley, John F. Wu, Camille Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, Andy D. Goulding, Tomasz Kacprzak, Peter Melchior, M. Pasquato, Nesar Ramachandra, Yuan-Sen Ting, G. van de Ven, S. Villar, V. A. Villar, E. Zinger:
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers. CoRR abs/2310.12528 (2023)

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