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arXiv Open Access 2020

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Abigail R. Azari John B. Biersteker Ryan M. Dewey Gary Doran Emily J. Forsberg +11 lainnya
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Abstrak

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.

Topik & Kata Kunci

astro-ph.IM astro-ph.EP stat.ML

Penulis (16)

A

Abigail R. Azari

J

John B. Biersteker

R

Ryan M. Dewey

G

Gary Doran

E

Emily J. Forsberg

C

Camilla D. K. Harris

H

Hannah R. Kerner

K

Katherine A. Skinner

A

Andy W. Smith

R

Rashied Amini

S

Saverio Cambioni

V

Victoria Da Poian

T

Tadhg M. Garton

M

Michael D. Himes

S

Sarah Millholland

S

Suranga Ruhunusiri

Format Sitasi

APA MLA BibTeX
Azari, A.R., Biersteker, J.B., Dewey, R.M., Doran, G., Forsberg, E.J., Harris, C.D.K. et al. (2020). Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade. https://arxiv.org/abs/2007.15129

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Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
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arXiv
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Open Access ✓