Learning from the machine
Haiman Z. 2018. Learning from the machine. Nature Astronomy. 3(1), 18–19.
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Journal Article
| Published
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Abstract
Large cosmological datasets have been probing the properties of our Universe and constraining the parameters of dark matter and dark energy with increasing precision. Deep learning techniques have shown potential to be smarter than — and greatly outperform — human-designed statistics.
Publishing Year
Date Published
2018-10-29
Journal Title
Nature Astronomy
Publisher
Springer Science and Business Media LLC
Volume
3
Issue
1
Page
18-19
ISSN
IST-REx-ID
Cite this
Haiman Z. Learning from the machine. Nature Astronomy. 2018;3(1):18-19. doi:10.1038/s41550-018-0623-9
Haiman, Z. (2018). Learning from the machine. Nature Astronomy. Springer Science and Business Media LLC. https://doi.org/10.1038/s41550-018-0623-9
Haiman, Zoltán. “Learning from the Machine.” Nature Astronomy. Springer Science and Business Media LLC, 2018. https://doi.org/10.1038/s41550-018-0623-9.
Z. Haiman, “Learning from the machine,” Nature Astronomy, vol. 3, no. 1. Springer Science and Business Media LLC, pp. 18–19, 2018.
Haiman Z. 2018. Learning from the machine. Nature Astronomy. 3(1), 18–19.
Haiman, Zoltán. “Learning from the Machine.” Nature Astronomy, vol. 3, no. 1, Springer Science and Business Media LLC, 2018, pp. 18–19, doi:10.1038/s41550-018-0623-9.
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