{"issue":"1","language":[{"iso":"eng"}],"article_type":"original","date_published":"2018-10-29T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.1038/s41550-018-0623-9"}],"status":"public","day":"29","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-09-05T10:01:23Z","author":[{"id":"7c006e8c-cc0d-11ee-8322-cb904ef76f36","last_name":"Haiman","first_name":"Zoltán","full_name":"Haiman, Zoltán"}],"page":"18-19","date_updated":"2024-09-18T08:51:45Z","citation":{"ama":"Haiman Z. Learning from the machine. <i>Nature Astronomy</i>. 2018;3(1):18-19. doi:<a href=\"https://doi.org/10.1038/s41550-018-0623-9\">10.1038/s41550-018-0623-9</a>","short":"Z. Haiman, Nature Astronomy 3 (2018) 18–19.","chicago":"Haiman, Zoltán. “Learning from the Machine.” <i>Nature Astronomy</i>. Springer Science and Business Media LLC, 2018. <a href=\"https://doi.org/10.1038/s41550-018-0623-9\">https://doi.org/10.1038/s41550-018-0623-9</a>.","apa":"Haiman, Z. (2018). Learning from the machine. <i>Nature Astronomy</i>. Springer Science and Business Media LLC. <a href=\"https://doi.org/10.1038/s41550-018-0623-9\">https://doi.org/10.1038/s41550-018-0623-9</a>","ista":"Haiman Z. 2018. Learning from the machine. Nature Astronomy. 3(1), 18–19.","ieee":"Z. Haiman, “Learning from the machine,” <i>Nature Astronomy</i>, vol. 3, no. 1. Springer Science and Business Media LLC, pp. 18–19, 2018.","mla":"Haiman, Zoltán. “Learning from the Machine.” <i>Nature Astronomy</i>, vol. 3, no. 1, Springer Science and Business Media LLC, 2018, pp. 18–19, doi:<a href=\"https://doi.org/10.1038/s41550-018-0623-9\">10.1038/s41550-018-0623-9</a>."},"oa_version":"None","publisher":"Springer Science and Business Media LLC","type":"journal_article","doi":"10.1038/s41550-018-0623-9","publication":"Nature Astronomy","year":"2018","publication_identifier":{"issn":["2397-3366"]},"month":"10","scopus_import":"1","quality_controlled":"1","title":"Learning from the machine","extern":"1","_id":"17544","volume":3,"article_processing_charge":"No","intvolume":"         3","abstract":[{"lang":"eng","text":"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."}],"publication_status":"published"}