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Conversely, when training DNNs, layers composing the neural networks are simultaneously trained using backpropagation.<\/jats:p>\n          <jats:p>In this paper, we argue that the training scheme of ML pipelines is sub-optimal because it tries to optimize a single operator at a time thus losing the chance of global optimization. We therefore propose WindTunnel: a system that translates a trained ML pipeline into a pipeline of neural network modules and jointly optimizes the modules using backpropagation. We also suggest translation methodologies for several non-differentiable operators such as gradient boosting trees and categorical feature encoders. Our experiments show that fine-tuning of the translated WindTunnel pipelines is a promising technique able to increase the final accuracy.<\/jats:p>","DOI":"10.14778\/3485450.3485452","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T23:26:50Z","timestamp":1642202810000},"page":"11-20","source":"Crossref","is-referenced-by-count":8,"title":["WindTunnel"],"prefix":"10.14778","volume":"15","author":[{"given":"Gyeong-In","family":"Yu","sequence":"first","affiliation":[{"name":"Seoul National University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed","family":"Amizadeh","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sehoon","family":"Kim","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artidoro","family":"Pagnoni","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"ETH Zurich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byung-Gon","family":"Chun","sequence":"additional","affiliation":[{"name":"Seoul National University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Weimer","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Interlandi","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148177"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330667"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 33rd International Conference on Machine Learning. 173--182","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei , Sundaram Ananthanarayanan , Rishita Anubhai , Jingliang Bai , Eric Battenberg , Carl Case , Jared Casper , Bryan Catanzaro , Qiang Cheng , Guoliang Chen , 2016 . 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Deep Neural Decision Trees. arXiv:1806.06988 [cs.LG]"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3485450.3485452","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:40:48Z","timestamp":1672224048000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3485450.3485452"}},"subtitle":["towards differentiable ML pipelines beyond a single model"],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["10.14778\/3485450.3485452"],"URL":"https:\/\/doi.org\/10.14778\/3485450.3485452","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2021,9]]}}}