{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bag-of-baselines-for-multi-objective-joint","title":"Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization","arxiv_id":"2105.01015","date":"2021-05-03","proceeding":"ICML Workshop AutoML 2021 7","authors":["Julia Guerrero-Viu","Sven Hauns","Sergio Izquierdo","Guilherme Miotto","Simon Schrodi","Andre Biedenkapp","Thomas Elsken","Difan Deng","Marius Lindauer","Frank Hutter"],"abstract":"Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural network to use and tuning the hyperparameters of the used training pipeline. 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