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Leveraging join sampling and modern deep autoregressive models, NeuroCard makes no inter-table or inter-column independence assumptions in its probabilistic modeling. NeuroCard achieves orders of magnitude higher accuracy than the best prior methods (a new state-of-the-art result of 8.5x maximum error on JOB-light), scales to dozens of tables, while being compact in space (several MBs) and efficient to construct or update (seconds to minutes).<\/jats:p>","DOI":"10.14778\/3421424.3421432","type":"journal-article","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T01:15:11Z","timestamp":1603847711000},"page":"61-73","source":"Crossref","is-referenced-by-count":158,"title":["NeuroCard"],"prefix":"10.14778","volume":"14","author":[{"given":"Zongheng","family":"Yang","sequence":"first","affiliation":[{"name":"UC Berkeley"}]},{"given":"Amog","family":"Kamsetty","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]},{"given":"Sifei","family":"Luan","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]},{"given":"Eric","family":"Liang","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]},{"given":"Yan","family":"Duan","sequence":"additional","affiliation":[{"name":"Covariant"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Covariant"}]},{"given":"Ion","family":"Stoica","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]}],"member":"320","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742797"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/767141.767147"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/376284.375685"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Durkan Conor","year":"2019","unstructured":"Conor Durkan and Charlie Nash . 2019 . 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