{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:01:53Z","timestamp":1750309313438,"version":"3.41.0"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2024,1,3]]},"abstract":"<jats:p>Hyperparameters refer to a set of parameters of a machine learning model that are fixed and not adjusted during training. A fundamental problem in this context is hyperparameter tuning which refers to the problem of identifying the best values for a set of model hyperparameters for a given task. In particular, model performance strongly depends on the choice of hyperparameters, and the right choice often determines the difference between average and stateof- the-art performance. This becomes especially important in models with many hyperparameters, as is common in deep learning models (DL) and automated machine learning (AutoML). However, finding the best set of hyperparameters for a model faced with a given task is very difficult in general, given the large state space and the high computational cost of assessing the quality of a given set of hyperparameters.<\/jats:p>","DOI":"10.1145\/3639830.3639833","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T23:34:15Z","timestamp":1704497655000},"page":"3-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning the Optimal Representation Dimension for Restricted Boltzmann Machines"],"prefix":"10.1145","volume":"51","author":[{"given":"Amanda Camacho","family":"Novaes de Oliveira","sequence":"first","affiliation":[{"name":"Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00848"},{"key":"e_1_2_1_2_1","first-page":"1","volume-title":"International Joint Conference on Neural Networks (IJCNN)","author":"A. C.","year":"2022","unstructured":"A. C. N. de Oliveira and D. R. Figueiredo. Network connectivity and learning performance on Restricted Boltzmann Machines. In International Joint Conference on Neural Networks (IJCNN), pages 1--8, 2022."},{"key":"e_1_2_1_3_1","volume-title":"Optimal connectivity through network gradients for the Restricted Boltzmann Machine","author":"de Oliveira A. C. N.","year":"2022","unstructured":"A. C. N. de Oliveira and D. R. Figueiredo. Optimal connectivity through network gradients for the Restricted Boltzmann Machine, 2022. Preprint available at arxiv.org\/abs\/2209.06932."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/1674-1056\/abd160"},{"key":"e_1_2_1_5_1","volume-title":"UCI machine learning repository","author":"Dua D.","year":"2017","unstructured":"D. Dua and C. Gra. UCI machine learning repository, 2017."},{"issue":"55","key":"e_1_2_1_6_1","first-page":"1","article-title":"Neural architecture search: A survey","volume":"20","author":"Elsken T.","year":"2019","unstructured":"T. Elsken, J. H. Metzen, and F. Hutter. Neural architecture search: A survey. Journal of Machine Learning Research, 20(55):1--21, 2019.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01064"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.05.025"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_32"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660859.2660946"},{"key":"e_1_2_1_11_1","series-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition","volume-title":"Foundations, page 194--281","author":"Smolensky P.","year":"1986","unstructured":"P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, page 194--281, 1986."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390290"}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639830.3639833","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639830.3639833","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:50Z","timestamp":1750291430000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639830.3639833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,3]]},"references-count":12,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1,3]]}},"alternative-id":["10.1145\/3639830.3639833"],"URL":"https:\/\/doi.org\/10.1145\/3639830.3639833","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2024,1,3]]},"assertion":[{"value":"2024-01-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}